# Regional Haze Plan

for

Florida Class I Areas

Prepared by

Florida Department of Environment Protection

Division of Air Resource Management

March 17, 2010

(Amended August 31 , 2010)

(Amended May xx, 2012)i 

### Preface: This document contains summaries of the technical analyses used by Florida ’s Department of Environmental Protection (DEP), Division of Air Resource Management ( DARM )

, to support the regional haze state implementation plan pursuant to §§107(d)(3)(D) and (E) of the Clean Air Act, as amended.ii TABLE OF CONTENTS

EXECUTIVE SUMMARY

................................ ................................ ................................ ........... vi

1.0 INTRODUCTION

................................ ................................ ................................ .................... 1
1.1 What is regional haze?

................................ ................................ ................................ .......... 1
1.2 What are the requirements under the Clean Air Act for addressing regional haze?

............. 1
1.3 General overview of regional haze SIP requirements

................................ .......................... 2

1.4 Class I areas in and near Florida

................................ ................................ ........................... 4
1.5 State and Federal Land Manager (FLM) coordination

................................ ......................... 5

2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND ESTIMATE OF

NATURAL BACKGROUND CONDITIONS IN CLASS I AREAS

................................ ... 8
2.1 Estimating Natural Conditions for Florida Class I Areas

................................ ..................... 9
2.2 Estimating Baseline Conditions for Florida Class I Areas

................................ ................. 10
2.3 Summary of Natural Background and Baseline Conditions for Florida Class I Areas

...... 10
2.4 Pollutant Contributions to Visibility Impairment (2000

- 2004 Baseline Data)

................... 11

3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064

................................ ...................... 15
3.1 Glidepaths for Class I Areas in Fl orida and Nearby States

................................ ................ 15

4.0 NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY IMPAIRMENT IN

FLORIDA CLASS I AREAS

................................ ................................ ............................... 21
4.1 Baseline Emissions Inventory

................................ ................................ ............................. 21
4.1.1 Stationary Point Sources

................................ ................................ ............................ 22
4.1.2 Stationary Area Sources

................................ ................................ ............................. 23

4.1.3 Off

- Road Mobile Sources

................................ ................................ .......................... 24
4.1.4 Highway Mobile Sources

................................ ................................ ........................... 24

4.1. 5 Biogenic Emission Sources

................................ ................................ ........................ 25
4.1.6 Summary 2002 Base G2 (February 2007 version) Baseline Emissions Inventory for

FL

................................ ................................ ................................ ................................ ..... 25
4.1.7 Model Performance Improvements through Emissions Inventory Improvements

.... 25
4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories

. 26

5.0 REGIONAL HAZE MODELING METHODS AND INPUTS

................................ ............. 27
5.1 Analysis Method

................................ ................................ ................................ ................. 27

5.2 Model Selection

................................ ................................ ................................ .................. 28

5.2.1 Selection of Photochemical Grid Model

................................ ................................ .... 28
5.2.2 Selection of Meteorological Model

................................ ................................ ............ 29
5.2.3 Selection of Emissions Processing System

................................ ................................ 30iii 5.3 Selection of the Modeling Year

................................ ................................ .......................... 31
5.4 Modeling Domains

................................ ................................ ................................ ............. 33
5.4.1 Horizontal Modeling Domain

................................ ................................ .................... 33
5.4.2 Vertical Modeling Domain

................................ ................................ ......................... 35

6. 0 MODEL PERFORMANCE EVALUATION

................................ ................................ ......... 35
6.1 Modeling Performance Goals, and Criteria

................................ ................................ ........ 37

6.2 VISTAS Domain

- Wide Performance

................................ ................................ ................. 38

6.3 Florida Class 1 Areas Performance

................................ ................................ .................... 41

7.0 LONG

- TERM STRATEGY FOR FLORIDA CLASS I AREAS

................................ .......... 48
7.1 Overview of the Long

- Term Strategy Development Process

................................ ............. 48

a. A ssuming implementation of existing federal and state air regulatory requirements in

Florida and the VISTAS region, how much visibility improvement, compared to the glidepath, would we expect to see at Class I areas in or near Florida between now and

2018?

................................ ................................ ................................ ................................ 48

b. If additional emission reductions were needed, from what pollutants and source

categories would the greatest visibility benef its be realized between the baseline and

2018?

................................ ................................ ................................ ................................ 48

d. What is the process for identifying additional emission controls or reductions that

represent reasonable progress for those specific sources?

................................ ............... 48

f. Given the additional emission reductions expected from BART and reasonable progress

improvements, how much additional visibility improvement, compared to the glidepath, will we expect to see at Class I areas in Florida between the baseline and 2018?

........... 48
7.2 Expected Visibility Results in 2018 for Florida Class I Areas under existing and planned emissions controls (G2 and, Best and Final Inventory (G4))

................................ ........... 49
7.2.1 Federal and State Control Requirements

................................ ................................ .... 49
7.2.2 Additional State programs to reduce emissions

................................ ......................... 51
7.2.3 Projected 2009 and 2018 G2 and Best and Final (G4) Emissions Inventories

.......... 52

7.2.4 Model Results for the 2018 Base Best and Final (G4) Inventory Compared to the

Uniform Rate of Progress Glidepaths for FL Class I Areas

................................ ............. 55

7.3 Relative Con tribution from International Emissions to Visibility Impairment in 2018 at

VISTAS Class I areas

................................ ................................ ................................ ....... 73

7.4 Relative Contributions to Vi sibility Impairment: Pollutants, Source Categories, and Geographic Areas

................................ ................................ ................................ ............. 75
7.5 Relative Contributions to Visibility Impairment: Geo graphic Areas of Influence for

Florida Class I Areas

................................ ................................ ................................ ........ 78
7.5.1 Back Trajectory Analyses

................................ ................................ .......................... 79

7.5.2 Residence Time Plots

................................ ................................ ................................ . 79

7.5.3 SO 2 Areas of Influence

................................ ................................ ............................... 80

7.5. 4 Emissions Sources within SO 2 Areas of Influence

................................ .................. 81iv 7.5.5 Specific Source Types in the Areas of Influence for Florida and Nearb y Class I

Areas

................................ ................................ ................................ ................................ 86
7.6 Reasonable Progress Requirement

................................ ................................ ...................... 90
7.7 Florida’s Reasonable Progress Analysis

................................ ................................ ............. 91

7.8 Best Available Retrofit Technology (BART) for Individual Sources

.............................. 101

7.8.1 BART

- Eligible Sources in Florida

................................ ................................ ......... 101

7.8.2 Florida’s BART Rule

................................ ................................ ............................... 103
7.8.3 Determination of Sources Subject to BART in Florida

................................ ........... 104
7.8.4 Determination of BART Requirements for Subject

- to

- BART Sources

................... 106

8.0 REASONABLE PROGRESS GOALS

................................ ................................ ................ 123

9. 0 MONITORING STRATEGY

................................ ................................ ............................... 124

10.0 INTERSTATE CONSULTATION

................................ ................................ .................... 127

11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONS

................. 127

12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN

................................ 129v LIST OF EXHIBITS

Exhibit 1: Rules Adopted by the State

Exhibit 2: BART Determinations and Permits, Permits Necessary for BART Exemption

, Reasonable Progress Determinations and Permits, and Verification of Shutdown s

Exhibit 3: Consultation Materials

LIST OF APPENDICES

Appendix A: VISTAS Memorandum of Understanding and Bylaws

Appendix B: Conceptual Descr iption

Appendix C: Modeling Analysis Protocol and Quality Assurance Project Plan

Appendix D: Emissions Inventory Documentation

Appendix E: Air Quality and Meteorological Preparations and Results

Appendix F 1 : Technical Support Document

– Emissions a nd Air Quality Modeling

Appendix F2: Meteorological Modeling Documentation

Appendix G: Global Modeling Documentation

Appendix H: Air Quality Modeling Sensitivity Analysis and Source Apportionment

Appendix I: Data Access

Appendix J: Analysis of Consent Decree and Voluntary Reductions

Appendix K: Recommended Improvements

Appendix L: Best Available Retrofit Technology (BART) Supporting Materials

Appendix M: Supporting Materials for Rule Development

Appendix N: Florida Smoke Management Planvi 

### EXECUTIVE SUMMARY

Pursuant to the Clean Air Act requirements contained in sections 169A and 169B, and the subsequent implementing regulations contained in 40 CFR 51.308, the S tate of Florida

, Department of Environmental Protection (DEP) has developed a p roposed State Implementation Plan (SIP) revision for regional haze. The SIP revision represents commitments and actions taken by the state affecting reasonable progress during the ten

- year period from 2008 to 2018 towards the goal of attaining natural visibility conditions by 2064. It also provides for the implementation of Best Available Retrofit Technology (BART).

To develop this proposed SIP revision , the state has relied heavily on the work of the Southeast regional planning group VISTAS (Visibili ty Improvement States and Tribal Association of the Southeast). VISTAS is directed by the state air directors of 10 southeastern states, including all of the eight U.S. Environmental Protection Agency ( EPA ) , Region 4 states plus Virginia and West Virginia

. O ne staff person is directly connected to VISTAS

– the technical coordinator. All other personnel resources are within the state programs. Funding for VISTAS was through grants from the U.S. EPA.

The data and analysis necessary to meet the requirem ents of the federal regional haze regulations are considerable. The 10 states, through VISTAS, completed most of the technical requirements using contracted resources, managed by the technical coordinator. To help direct the technical work, the state dir ectors created three workgroups. To e ffect an open and inclusive direction of work, the workgroups consisted of federal, state, and private stakeholder representatives. The federal representatives included EPA regions 3 and 4, and federal land managers ( or their representatives) from the National Park Service, the Fish and Wildlife Service, and the Forest Service. Private stakeholders included industry representatives with sources within the 10

- state planning region. These workgroups discussed and revie wed the work completed by the contractors used by VISTAS. These data and analyses produced by VISTAS form the technical

basis for Florida’s proposed SIP revision

.

The Florida regional haze SIP consists of a set of rules, commitments, and plans addressin g the requirements of the federal regulations. It also contains the administrative and technical documentation supporting these rules, commitments, and plans. The documents to be incorporated into the SIP include the Regional Haze Plan (i.e., the main bo dy of this report ), Exhibit 1, Exhibit 2 (excluding those elements of the permits not specifically related to the BART emission limits) , and Exhibit 3. The primary elements of the Florida regional haze SIP are the following:

1. Rules 62

- 296.340, Florida Administrative Code (F.A.C.) (new), and 62

- 204.800, F.A.C. (amended)

– These state rules implement the Best Available Retrofit Technology (BART) requirements. The state rule adopts the federal rule by reference (62

- 204.800), and adds specific

state

- deter mined criteria as needed or required by the federal rule (62

- 296.340). For example, the state BART rule is implemented through the state’s air construction permit program, and the rule includes required application submission dates and BART compliance da tes. The rule alsovii makes certain decisions required of the federal regulations such as allowing BART to equal CAIR (Clean Air Interstate Rule) for electric generating units and excluding ammonia and volatile organic compounds from BART review.

The depart ment has evaluated 4 6 facilities having units that meet the BART applicability requirements. Of these, 33 meet the exemption criteria in the rule, 7 have been shut down, and 6 have completed BART determinations.

2. Reasonable Progress Requirements

– Th e state is required to consider four factors (cost, time to comply, energy and non

- air impacts, and remaining useful life) in determining whether further reductions in visibility

- impairing pollutants would be reasonable for any sources in the state. To

li mit the scope of this requirement, and based on VISTAS analysis, the department focused its response to reasonable progress on sulfur dioxide (SO 2 ) emissions from large energy generating

and industrial sources. Based on criteria to identify

, from among th ese sources, those that are most affecting visibility in Class I areas, 32 emissions units from 15 major facilities were targeted for four

- factor review. The state then considered that 31 of these units were subject to

the CAIR rule, and the other unit is subject to the proposed boiler MACT rule. The emissions reduction requirements under both of these rules were developed in a manner generally

consistent with the four factors prescribed for reasonable progress ( see section 7. 7 ). As such, we

assert that the reasonable progress requirements are satisfied for Florida’s identified sources with

the federal CAIR and the boiler MACT rules.

Of the 31 CAIR units subject to reasonable progress review, nine are currently shutdown and two
unit s have taken federally enforceable permit condition s that limit SO 2 emissions such that they

are no longer applicable to reasonable progress review

.

3. Reasonable Progress Goals

– The state is required to set reasonable progress goals in units of deciviews applicab le for 2018. These goals represent the progress (visibility improvement) expected as a result of implementation of the long

- range plan presented in this regional haze SIP. Two goals are set for each Class I area, one for the 20 percent worst days and one for the 20 percent best days.

Visibility (dv) Baseline (00

- 04) Natural Conditions 2018 Reasonable Progress

Goals
Class I Area Best

20% Worst

20% Best

20% Worst

20% Best 20% Worst 20%

Chassahowitzka 15.51 25.75 5.91 11.03 13.91 22.27 Everglades 11.69 22.30 5.21 12.09 11.46 19. 8 0

Saint Marks 14.37 26.31 5.39 11.67 12.80 2 2

. 92

These goals are based upon predicted visibility response to the expected emissions reductions of visibility

- impairing pollutants using air quality models. As a result, a fair amount of uncertainty surrounds each of these goals, but they represent the stat e’s best estimate at this time.viii 4. Rate of Progress Needed to Attain Natural Visibility Conditions by 2064

– Using the current estimate of natural conditions for the worst days, the uniform and predicted rate s of progress for

each of the Florida Class I areas is:
Worst 20 Percent Days

Class I Area Uniform Rate of Progress (dv/yr) 2018 Uniform

Rate of Progress (dv) 2018 Predicted

Rate of Progress (dv/yr) 2018 Predicted Visibility (dv)

Chassahowitzka 0.25 22.31 0.25 22.27 Everglades 0.17 19.92 0.1 8 19. 8 0

Saint Marks 0.24 22.89 0.24 2 2

. 92

The regulatory requirement for the best 20 percent visibility days is that these days not get

worse.

Best 20 Percent Days

Class I Area Uniform Rate of Progress (dv/yr) 2018 Uniform

Rate of Progress (dv) 2018 Predicted

Rate of Progress (dv/yr) 2018 Predicted Visibility (dv)

Chassahowitzka 0.00 15.51 0.11 13.91 Everglades 0.00 11.69 0.02 11.46

Saint Marks 0.00 14.37 0.11 12.80

Each of the three Florida Class I areas are predicted to progress at approximately the nominal uniform rate of progress for the initial planning period. Rates of progress needed to meet the natural visibility goal for subsequent planning periods will be d ependent on the actual progress made in the previous planning periods.

5. Long

- Term Strategy for Regional Haze

– The state has developed a long

- term strategy that largely relies on existing rules and programs that will result in significant improvement of visibility in Class I areas. The most significant of these existing rules is the Clean Air Interstate Rule (CAIR), which in conjunction with other existing rules results in visibility improvement in all the Class I areas equal to the nominal uniform r ate of progress toward the national goal of

natural conditions. But the long

- term strategy also includes new reductions of visibility precursor emissions resulting from the Best Available Retrofit Technology (BART) requirements of the regional haze rule and the proposed new federal rule for Industrial, Commercial, and Institutional Boilers and Process Heaters (Boiler MACT rule)

. Initial

c ompliance with these rules will occur at various times throughout the planning period, but no

later than the end of 2 013

.

Through the Southeast regional planning organization VISTAS, state

- of

- the

- art air quality modeling was completed to analyze the regional, national, and global contributions to visibility in each Class I area. Emissions of visibility

- impairing pol lutants were included from all known source sectors and locations, including boundary conditions derived from a global model. Current visibility conditions were evaluated using data from public and private monitoring networks, and these and other associat ed data were used to validate model performance.ix Projected emissions were developed for 2018, considering growth and known or estimated emissions changes due to existing regulations. Substantial analysis was completed to determine visibility sensitivity to specific pollutant reductions, and to parse

- out the source

- sector
contributions.

The conclusion of this analysis is that the long

- term strategy for attaining the 2064 visibility goal can rely on existing and proposed new regulations to provide reaso nable progress toward th is goal during the first ten

- year planning period ending in 2018

.

6. Commitments

– The state is committing to complete a mid

- point review of the long

- range plan as required in the regional haze rule (§51.308(g)). The time of the review will be coordinated with the other VISTAS states to take advantage of common analyses, but will be no later than five years after submission of the initial implementation plan. We will review the progress of the projected emissions changes to j udge the necessity of making any revisions to the plan. The state is also committing to complete comprehensive periodic revisions of the implementation plan for regional haze. The first revision is due to EPA by July 31, 2018 and every ten years therea fter.1 

### 1.0 INTRODUCTION

1.1 What is regional haze?

Regional haze is pollution from disparate sources that impairs visibility over a large region, including national parks, forests, and wilderness areas (federal “Class I” areas). Regional haze is caused by sources and activities emitting fine particles and their precursors. Those emissions are often transported over large regions.

Particles affect visibility through the scattering and absorption of light, and fine particles

– particles similar in size to the wavelength of light

– are most efficient, per unit of mass, at reducing visibility. Fine particles may either be emitted directly or formed from emissions of precursors, the most important of which are sulfur dioxides (SO 2 ) and nitrogen oxides (NOx). Reducing fine particles in the atmosphere is gen erally considered to be an effective method of reducing regional haze, and thus improving visibility. Fine particles also adversely impact human health, especially respiratory and cardiovascular systems. The United States Environmental Protection Agency (USEPA) has set national ambient air quality standards for daily and annual levels of fine particles with diameter smaller than 2.5 **μ** m (PM2.5). The most important sources of PM2.5 and its precursors are coal

- fired power plants, industrial boilers and

oth er combustion sources. Other significant contributors to PM2.5 and visibility impairment include mobile source emissions, area sources, fires, and wind blown dust.

1.2 What are the requirements under the Clean Air Act for addressing regional haze?

In Sec tion 169A of the 1977 Amendments to the Clean Air Act (CAA), Congress set forth a program for protecting visibility in Federal Class I areas which calls for the “prevention of any future, and the remedying of any existing, impairment of visibility in manda tory Class I Federal areas which impairment results from manmade air pollution.” Congress adopted the visibility provisions to protect visibility in 156 national parks and wilderness areas. On December 2, 1980, USEPA promulgated regulations to address vi sibility impairment (45 FR 80084). The 1980 regulations were developed to address visibility impairment that is “reasonably attributable” to a single source or small group of sources. These regulations represented the first phase in addressing visibility impairment and deferred action on regional haze that emanates from a variety of sources until monitoring, modeling and scientific knowledge about the relationships between pollutants and visibility impairment improved.

In the 1990 Amendments to the CAA , Congress added section 169B and called on USEPA to issue regional haze rules. The Regional Haze rule (RHR) that USEPA promulgated on July 1, 1999 (64 FR 35713), revised the existing visibility regulations in order to integrate provisions addressing regi onal haze impairment and establish a comprehensive visibility protection program for Class I Federal areas. States are required to submit state implementation plans (SIPs) to USEPA that set out each states’ plan for complying with the regional haze rule, including consultation and coordination with other states and with federal land managers. The timing of2 the SIP submittal is tied to USEPA’s promulgation of designations for the National Ambient Air Quality Standard (NAAQS) for fine particulate matter (PM 2.5) at 40 CFR Part 81. States must submit a regional haze implementation plan to USEPA within three years after the date of designation. Because USEPA promulgated designation dates on December 17, 2004, regional haze SIPs must be submitted by December 1 7, 2007 , which is specified at 40 CFR 51.308(b)

.

The regional haze rule addressed the combined visibility effects of various pollution sources over a wide geographic region. This wide reaching pollution net meant that many states

– even those

without Cla ss I Areas

– would be required to participate in haze reduction efforts. USEPA designated five Regional Planning Organizations (RPO) to assist with the coordination and cooperation needed to address the visibility issue. Those states that make up the so utheastern portion of the contiguous United States are known as VISTAS (Visibility Improvement

– State and Tribal Association of the Southeast), and include the following states: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carol ina, Tennessee, Virginia, and West Virginia.

Figure 1.2

- 1. Geographical Areas of Regional Planning Organizations

1.3 General overview of regional haze SIP requirements

The regional haze rule (RHR) at 51.308(d) requires states to demonstrate reasonab le progress toward meeting the national goal of a return to natural visibility conditions by 2064. As a guide for reasonable progress, the RHR directs states to graphically show what would be a “uniform rate of progress” toward natural conditions for each mandatory Class I Federal area within the State and/or for each mandatory Class I Federal area located outside the State, which may be affected by emissions from sources within the State. States are to establish baseline visibility3 conditions for 2000

- 20 04, natural background visibility conditions in 2064, and the rate of uniform progress between baseline and background conditions. The uniform rate of progress is also known as the “glidepath.”

The RHR then requires States to establish reasonable progr ess goals (RPGs), expressed in deciviews, for visibility improvement at each affected Class I area covering each (approximately) 10

- year period until 2064. The goals must provide for reasonable progress towards achieving natural visibility conditions, prov ide for improvement in visibility for the most impaired days over the period of the implementation plan, and ensure no degradation in visibility for the least impaired days over the same period (see §51.308(d)(1)).

In order to ensure that visibility goals are properly met and set, state implementation plans must include determinations, for each Class I area, of the baseline visibility conditions (expressed in deciviews) for the most impaired and least impaired days. SIPs must also contain supporting documentation for all required analyses used to calculate the degree of visibility impairment under natural visibility conditions for the most impaired and least impaired days (see §51.308(d)(2)). In addition, states must include a monitoring strategy for measuring, characterizing, and reporting of regional haze visibility impairment that is representative of all mandatory Class I Federal areas within the state (see §51.308(d)(4)).

This first set of reasonable progress goals must be met through measures c ontained in the state’s long

- term strategy covering the period from the present until 2018. The long

- term strategy includes enforceable emissions limitations, compliance schedules, and other measures as necessary to achieve the reasonable progress goals, including all controls required or expected under all federal and state regulations by 2009 and by 2018. During development of the long

term strategy, states are also required to consider specific factors such as the abovementioned ongoing control progra ms, measures to mitigate construction activities, source retirement and replacement schedules, smoke management programs for agriculture and forestry, and enforceability of specific measures (see §51.308(d)(3)).

In addition, a specific component of each state’s first long

- term strategy is dictated by the specific best available retrofit technology (BART) requirements in 40 CFR 51.308(e) of the RHR. The RHR at §51.308(e) requires states to include a determination of BART for each

BART

- eligible source in t he State that emits any air pollutant, which may reasonably be anticipated to cause or contribute to any impairment of visibility in any mandatory Class I

Federal area. Clean Air Act section 169A(b) defines BART

- eligible sources as sources in 26 specific source categories, in operation within a 15

- year period prior to enactment of the 1977 Clean Air Act Amendments. States must determine BART according to five factors set out in section 169A(g)(7) of the Clean Air Act. Emission limitations representing BA RT and schedules for compliance with BART for each source subject to BART must be included in the long

- term
strategy.

State Implementation Plans for the first review period are due December 17, 2007. These plans will cover long

- term strategies for visibi lity improvement between baseline conditions in 2000

- 2004 and 2018. States are required to evaluate progress toward reasonable progress goals every 5 years to assure that installed emissions controls are on track with emissions reduction forecasts4 in each SIP. The first interim review would be due to USEPA in December 2012. If emissions controls are not on track to meet SIP forecasts, then states would need to take action to assure emissions controls by 2018 will be consistent with the SIP or to revise t he SIP to be consistent

with the revised emissions forecast.

1.4 Class I areas in and near Florida

Florida has four Class I areas within its borders: Everglades National Park, Chassahowitzka Wilderness Area, St. Marks Wilderness Area, and Bradwell Ba y Wilderness Area

. Bradwell Bay is one of only two Class I areas in the country for which visibility is not considered an important value. As such, the RHR does not apply to Bradwell Bay. Florida air pollution sources are

within 300 kilometers of three other Class I areas located in other states, for which consideration of visibility impact on these out

- of

- state Class I areas must be addressed in the SIP. These are

Okefenokee Wilderness Area (Georgia) , Wolf Island Wilderness Area (Georgia) , and Breton

Wilderness Area (Louisiana)

. The Florida Division of Air Resource Management ( DARM ) in

the Florida Department of Environment al Protection (FLDEP) is responsible for developing the Regional Haze SIP. This SIP establishes reasonable progress goals for visi bility improvement at

each of the Florida Class I areas, and a long

- term strategy that will achieve those reasonable progress goals within the first regional haze planning period.

Figure 1.4

- 1. Florida and Nearby Class I areas5 In developing this SIP, we have also considered that emission sources outside of Florida may affect visibility at these Florida Class I areas, and that emission sources within Florida may affect visibility at the Class I areas in neighboring states. Through VISTAS, the southeast ern states have worked together to assess state

- by

- state contributions to visibility impairment in specific class I areas, including those in Florida and those affected by emissions from Florida . This technical work is discussed further in chapters 5, 6, a nd 7 below. Consultations to date between

Florida and other states are summarized in chapter 10; consultations are ongoing.

1.5 State and Federal Land Manager (FLM) coordination

As required by 40 CFR §51.308(i), the regional haze SIP must include proce dures for continuing consultation between the States and Federal Land Managers on the implementation of the visibility protection program, including development and review of implementation plan

revisions and 5

- year progress reports, and on the implementat ion of other programs having the potential to contribute to impairment of visibility in any mandatory Class I Federal area within

the State. DEP commits to ongoing consultation with the FLMs throughout the implementation process, including annual discussi on of the implementation process and the most recent IMPROVE monitoring data and VIEWS data.

Successful implementation of a regional haze program will involve long

- term regional coordination among States. VISTAS was formed in 2001 to address regional haz e and visibility problems in the southeastern United States. Jurisdictions represented by VISTAS members include the Eastern Band of Cherokee Indians; the States of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennesse e, Virginia, and West Virginia; and the local air pollution control programs located in these States. A copy of the VISTAS Memorandum of Agreement and Bylaws is enclosed as Appendix A.

The objectives of the VISTAS project are to establish natural backgro und visibility conditions across the mandatory Class I Federal areas, identify current visibility impairment levels, analyze emission control levels that will achieve interim visibility goals, and provide adequate documentation to member agencies so that t hey can develop their regional haze State/Tribal Implementation Plans (SIP/TIP). Figure 1.5

- 1 shows the 18 mandatory Class I Federal areas in the VISTAS Region, where visibility is an important value. Figure 1.5

- 2 lists these Class I areas.6 Figure 1 .5

- 1. Class I Areas in the VISTAS Region7 Mandatory Class I Federal Areas in the VISTAS Region Where Visibility is an Important Value

State Area Name Acreage Federal

Land Manager 40 CFR §81.401 Alabama Sipsey Wilderness 12,646 USDA

- FS
40 CFR §81.407 Florida Chassahowitzka Wilderness 23,360 USDI

FWS Everglades National Park 1,397,429 USDI

- NPS

St. Marks Wilderness 17,745 USDI

FWS 40 CFR §81.408 Georgia Cohotta Wilderness 33,776 USDA

- FS

Okefenokee Wilderness 343,850 USDI

FWS

Wolf Island Wilderness 5,126 USDI

FWS 40 CFR §81.411 Kentucky Mammoth Cave National Park 51,303 USDI

- NPS
40 CFR §81.422 North

Carolina Great Smoky Mountains National

Park 273,551 USDI

- NPS

Joyce Kilmer

- Slickrock Wilderness 10,201 USDA

- FS Linville Gorge Wilderness 7,575 USDA

- FS Shining Rock Wilderness 13,350 USDA

- FS Swanquarter Wilderness 9,000 USDI

FWS
40 CFR §81.426 South

Carolina Cape Romain Wilderness 28,000 USDI

FWS
40 CFR §81.428 Tennessee Great Smoky Mountains National

Park 241,207 USDI

- NPS

Joyce Kilmer

- Slickrock Wilderness 3,832 USDA

- FS 40 CFR §81.433 Virginia James River Face Wilderness 8,703 USDA

- FS

Shenandoah National Park 190,535 USDI

- NPS
40 CFR §81.435 West Virginia Dolly Sods Wilderness 10,215 USDA

- FS

Otter Creek Wilderness 20,000 USDA

- FS

Figure 1.5

- 2. Mandatory Class I Federal Areas in the VISTAS Region Where Visibility is an Important Value

A technical support document for the regional haze state implementation plans has been prepared cooperatively by VISTAS staff and states to characterize regional haze in the southeastern United States. The report includes a review of the science and situation, calculation of initial8 baseline visibility, review of monitoring data/data gaps, and recommendations for additional monitoring, initial emission inventory characterization and projections, and compliance with existing control programs. Source contributions to VISTAS mandatory Class I Federal areas are

also assessed. This document is included as Appendix F1.



### 2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND

ESTIMATE OF NATURAL BACKGROUND CONDITIONS IN CLASS I

AREAS

The goal of the Regional Haze Rule is to restore natural visibility conditions to the 156 Class I

areas identified in the 1977 Clean Air Act Amendments. Sec. 51.301(q) defines natural conditions: “Natural conditions include naturally occurring phenomena that reduce visibility as measured in terms of light extinction, visual range, contrast, or coloration.” The Regio nal Haze SIPs must contain measures that make “reasonable progress” toward this goal by reducing anthropogenic emissions that cause haze.

An easily understood measure of visibility to most people is visual range. Visual range is the greatest distance, i n kilometers or miles, at which a dark object can be viewed against the sky. For evaluating the relative contributions of pollutants to visibility impairment, however, the most useful measure of visibility impairment is light extinction, which is usually expressed in units of inverse megameters (Mm - 1 ). Light extinction affects the clarity and color of objects being

viewed.

The measure used by the regional haze rule is the deciview (dv). Deciviews are calculated directly from light extinction using a log arithmic scale. The deciview is a useful measure for tracking progress in improving visibility, because each deciview change is an equal incremental change in visibility perceived by the human eye. Most people can detect a change in visibility at

one dec iview.

For each Class I area, there are three metrics of visibility that are part of the determination of reasonable progress:

1) natural conditions, 2) baseline conditions, and 3) current conditions.

Each of the three metrics includes the concentration data of the visibility pollutants as different terms in the light extinction algorithm, with respective extinction coefficients and relative humidity factors. Total light extinction when converted to deciviews (dv) is calculated for the average of the 20 perc ent best and 20 percent worst visibility days.

“Natural” visibility is determined by estimating the natural concentrations of visibility pollutants and then calculating total light extinction. “Baseline” visibility is the starting point for the9 improvem ent of visibility conditions. It is the average of the Interagency Monitoring of

Protected Visual Environments (IMPROVE) monitoring data for 2000 through 2004 and is equivalent to “current” visibility conditions for this initial review period. The compar ison of initial baseline conditions to natural visibility conditions indicates the amount of improvement necessary to attain natural visibility by 2064. Each state must estimate natural visibility levels

for Class I areas within its borders in consultatio n with Federal Land Managers and other states (51.308(d)(2)). “Current conditions” are assessed every five years as part of the SIP review where actual progress in reducing visibility impairment is compared to the reductions committed

to in the SIP.

2.1 Estimating Natural Conditions for Florida Class I Areas

Natural background visibility, as defined in 2003 USEPA guidance, is based on annual average concentrations of fine particle components. The same annual average natural background visibility is assum ed for all Class I areas in the eastern United States (separate values are estimated for the western United States). Natural background visibility for the 20 percent worst days is estimated by assuming that fine particle concentrations for natural backgro und are normally distributed and the 90 th percentile of the annual distribution represents natural background visibility on the 20 percent worst days.

In the 2003 guidance, USEPA also provided that states may use a “refined approach” to estimate the values that characterize the natural visibility conditions of the Class I areas. The purpose of such a refinement would be to provide more accurate estimate s with changes to the extinction algorithm that may include the concentration values, factors to calculate extinction from a measured particular species and particle size, the extinction coefficients for certain compounds, geographical variation (by altitu de) of a fixed value, and the addition of visibility pollutants.

In 2005 the IMPROVE Steering Committee made recommendations for a refined equation that modifies the terms of the original equation to account for the most recent data. The choice

betw een use of the old or the new equation for calculating the visibility metrics for each Class I area is made by the state in which the Class I area is located.

The new IMPROVE equation accounts for the effect of particle size distribution on light

extincti on efficiency of sulfate, nitrate, and organic carbon. The mass multiplier for organic carbon (particulate organic matter) is increased from 1.4 to 1.8. New terms are added to the equation to account for light extinction by sea salt and light absorption by gaseous nitrogen

dioxide. Site

- specific values are used for Rayleigh scattering to account for the site

- specific effects of elevation and temperature. Separate relative humidity enhancement factors are used for small and large size distributions of am monium sulfate and ammonium nitrate and for sea salt. The elemental carbon (light

- absorbing carbon), fine soil, and coarse mass terms do not change between the original and new IMPROVE equation.

The VISTAS states chose to use the new IMPROVE equation as the basis for the conceptual description because it takes into account the most recent review of the science and because it is10 recommended by the IMPROVE Steering Committee. For more detailed discussion of the two IMPROVE equations, see Appendi cies B and L

.

2.2 Estimating Baseline Conditions for Florida Class I Areas

Baseline visibility conditions at each Florida Class I area are estimated using sampling data collected at IMPROVE monitoring sites at the three Class I areas in Florida . A five year average (2000 to 2004) was calculated for each of the 20 percent worst and 20 percent best visibility days in accordance with 40 CFR 51.308(d)(2) and the USEPA Guidance for Tracking Progress Under the Regional Haze Rule. When less than five years of data had been collected, an acceptable average was calculated if at least three complete years were available. When less than three years of data were available data substitution methods, as descibed in Appendix B, were used to provide sufficient data. I MPROVE data records for the Everglades had four years of complete data and no substitution of data was made. However, Chassahowitzka and St. Marks both required data substitution to make their records complete. The light extinction and deciview visibility values for the 20 percent worst and 20 percent best visibility days at the Class I areas are based on data and calculations included in Appendix B of this SIP.

2.3 Summary of Natural Background and Baseline Conditions for Florida Class I Areas

Table 2.3

- 1 present s estimated natural background and baseline visibility metrics for Florida

Class I areas. Note that Florida is not considering international emissions to be a component of natural background. Baseline visibility on the 20 percent worst days is significantly better at Everglades (22.3 dv) than Chassahowitzka (25.7 dv) or St. Marks (26.3). On the other hand,

n atural background visibility is slightly worse for Everglades (12. 1 dv) than either Chassahowitzka ( 11. 0 dv) or St. Marks (11. 7 dv)

. Vi sibility in the northern Florida Class I areas is generally better than the other Southeastern Class I areas, but the Everglades is significantly better than any of the Southeastern areas.11 Table 2.3

- 1 Natural Background and Baseline Conditions for Flori da Class I Areas

Natural Background Conditions

Class 1 area Average for 20 percent Worst Days (deciviews) Average for 20 percent Best Days (deciviews) Average for 20 percent Worst Days Bext (Mm

- 1) Average for 20 percent Best Days Bext (Mm

- 1) Everglades N.P. 12. 1 5.2 42.7 16.9

Chassahowitzka W.A. 11. 0 5. 9 30.5 18.1

St. Marks W .A. 11. 7 5.4 33.1 17.2

Baseline Visibility Conditions 2000

- 2004

Class 1 area Average for 20 percent Worst Days (deciviews) Average for 20 percent Best Days (deciviews) Bext (Mm

- 1) Average for 20 percent Worst Days Bext (Mm

- 1) Average for 20 percent Best Days Everglades N.P. 22.3 11.7 116.4 32.3

Chassahowitzka W.A. 25.7 15.5 135.0 47.7

St. Marks W.A. 26.3 14.4 142.1 42.7

2.4 Pollutant Contributions to Visibility Impairment (2000

- 2004 Baseline Data)

The 20 percent best and worst visibility days at the Florida sites occur throughout the year . The worst days are associated with high sulfate levels, with many of the extremely bad visibility days often associated with high organic carbon levels due to fires.

Ammonium sulfate , (NH 4 ) 2 SO 4 , is the most important contributor to visibility impairment and fine particl e mass on the 20 percent worst and 20 percent b est visibility days at all the Florida Class I areas. Sulfate levels on the 20 percent worst days at Chassahowitzka and St. Marks

account s for 6 0

- 70 percent of the visibility impairment. At the Everglades, s ulfates account for

30

- 60 percent of the impairment. Across the VISTAS region, sulfate levels are higher at the Southern Appalachian sites than at the coastal sites of which Florida is grouped (Figure 2.4

- 1). And the sulfate levels in the Everglades are significantly lower than even Chassahowitzka or St.

Marks. On the 20 percent clearest days, sulfate levels are a little more uniform across the region (Figure 2.4

- 2) , however, it still remains a relatively large fraction of the total visibility impairment

.12 Figure 2.4

- 1. Average light extinction for the 20 percent Haziest Days in 2000

- 2004 at VISTAS and neighboring Class I areas using New IMPROVE equation

Figure 2.4

- 2. Average light extinction for the 20 percent Clearest Days in 2000

- 2004 at VISTAS and neighboring Class I areas using New IMPROVE equation VISTAS coastal VISTAS inland Neighboring non

- VISTAS 0 50 100 150 200 250 300Swanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat SmokyMtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, ARExtinction (Mm-1)

Sea Salt

Coarse

Soil

EC

POM

NH4NO3 (NH4)2SO4 Rayleigh

VISTAS coastal VISTAS inland Neighboring non

- VISTAS 0 50 100 150 200 250 300Swanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat SmokyMtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, ARExtinction (Mm-1)

Sea Salt

Coarse

Soil

EC

POM

NH4NO3 (NH4)2SO4 Rayleigh

0 10 20 30 40 50 60Extinction (Mm-1)

Sea Salt

CM

Soil

EC

POM

NH4NO3 (NH4)2SO4
Rayleigh

VISTAS coastal VISTAS inland Neighboring non

- VISTASSwanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat SmokyMtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, AR 0 10 20 30 40 50 60Extinction (Mm-1)

Sea Salt

CM

Soil

EC

POM

NH4NO3 (NH4)2SO4
Rayleigh

VISTAS coastal VISTAS inland Neighboring non

- VISTASSwanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat SmokyMtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, AR13 Sulfate particles are formed in the atmosphere from sulfur dioxide emissions. Sulfate particles occur as hydrogen sulfate, H 2 (SO 4 ), ammonium bisulfate HNH 4 SO 4 , and ammonium sulfate, (NH 4 ) 2 SO 4 , depending on the availability of ammonia, NH3, in the atmosphere.

Particulate Organic Matter (POM) is the second most important contributor to fine particle mass and light extinction on the 20 percent haziest and the 20 percent clearest days at the Fl orida Class I areas. Elevated levels of POM and Elemental Carbon, EC, indicate impact from wildfires or prescribed fire. Significant fire impacts are frequent enough to affect the Class I areas in Florida

. Fires tend to be episodic and associated with d ry conditions so year to year impacts can vary considerably. VISTAS collected additional samples of carbon at five sites, including Great Smoky Mountains and Millbrook, (Raleigh, NC), to better understand sources contributing to

carbon in rural and urban areas. At Great Smoky Mountains and other mountain Class I areas the majority of carbon is from either biogenic emissions from vegetation or from wood burning. Wood burning is more important in the fall, winter, and early spring months. Biogenic

emission s, chiefly emissions of volatile organics (VOC) from vegetation, peak in spring and summer. Biogenic carbon emissions at Cape Romain, SC, a coastal site similar to Swanquarter, were lower than emissions at the forested mountain sites. Carbon from gasolin e and diesel engines is a relatively small contribution at the rural sites. At Millbrook, carbon from fossil fuel combustion is a larger percentage contribution than at the rural sites, but still less than 50 percent

of total carbon measured. These resul ts suggest that controlling anthropogenic sources of carbon will have little benefit in improving visibility in Class I areas. Controlling anthropogenic sources of carbon will likely be more effective to reduce levels of PM2.5 in urban areas.

Ammonium nitrate , NH 4 NO 3 , is formed in the atmosphere by reaction of ammonia and nitrogen oxides. In the VISTAS region, nitrate formation is limited by availability of ammonia and by temperature. Ammonia preferentially reacts with sulfur dioxide and sulfate befor e reacting with nitrogen oxides. Particle nitrate is formed at lower temperatures; at elevated temperatures nitric acid remains in gaseous form. For this reason, particle nitrate levels are very low in the summer and a minor contributor to visibility imp airment. Particle nitrate concentrations are higher on winter days and are more important for the coastal sites where 20 percent worst days can occur on winter days. Nitrogen oxides are emitted by fossil fuel combustion by point, area, on road,

and non ro ad sources. Modeling data (see section 7) indicate that in the VISTAS region ammonium nitrate formation is limited by ammonia concentrations and suggest that for winter days, controls of ammonia sources would be more effective in reducing ammonium nitrate levels than controls of nitrogen oxides.

Elemental Carbon, EC, is a comparatively minor contributor to visibility impairment. Sources include agriculture, prescribed, wildland, and wild fires and incomplete combustion of fossil fuels. EC levels are higher at urban monitors than at the Class I areas and suggest controls of

fossil fuel combustion sources would be more effective to reduce PM2.5 in urban areas than to improve visibility in Class I areas.

Soil fine particles are minor contributors to visibility impairment at most sites on most days.

Occasional e pisodes of elevated fine soil can be attributed to Saharan dust episodes, particularly at Everglades. No control strategies are indicated for fine soil.14 Sea salt , NaCl, is observed at the coastal sites. Sea salt contributions to visibility impairment are most important on the 20 percent clearest days when sulfate and POM levels are low. Sea salt levels do not contribute significantly to visibility on the 20 percent worst visibility days. The new IMPROVE equation uses Chloride ion, Cl - , from routine I MPROVE measurements to

calculate sea salt levels. Cl may react with nitrate and volatize off the filter, so that Cl - may underestimate sea salt levels. VISTAS funded sodium ion, Na + , analyses of the IMPROVE

filters from Oct 2003 to Dec 2004 for nine sit es. Sodium ion measures may overestimate sea salt levels because sodium nitrate is an additional source of sodium ion that cannot be distinguished from sodium chloride. Our best understanding to date suggests that using Cl may underestimate

sea salt lev els by as much as a factor of two. This uncertainty has greatest implications for calculating sea salt levels under natural visibility conditions.

Coarse particle mass (particles with diameters between 2.5 and 10 microns) has a relatively

small contrib ution to visibility impairment because the light extinction efficiency of coarse mass is very low compared to the extinction efficiency for sulfate, nitrate, and carbon.

An unidentified component is reported by IMPROVE as the difference between the tota l PM2.5 mass measured on the filter and the sum of the measured components. This unidentified mass may be positive or negative and is attributable to water and/or the factors used to calculate molecular weights of the other components.

The New IMPROVE equation generally results in higher calculated light extinction on days with higher mass and lower light extinction on days with lower mass. This tends to increase calculated light extinction for current conditions and to decrease calculated light extin ction for natural visibility conditions. Adding sea salt to the new IMPROVE equation increases light extinction for both current and natural visibility conditions. Increasing the mass multiplier for particulate organic matter in the new IMPROVE equation increases light extinction for current conditions more than for natural conditions. The new algorithm does not change the conclusion that in the VISTAS region and in Florida , the most effective means to improve visibility is to

reduce sulfate concentratio ns.

PM2.5 trends in urban and Class I areas: IMPROVE data were compared to monitoring data from the Speciated Trends Network (STN) in nearby urban areas to understand the similarities and differences in composition of fine particle mass. Several PM2.5 n onattainment areas are in close proximity to the Class I areas in the southeastern US, including Atlanta, GA; Birmingham, AL; Charleston, WV; Chattanooga, TN; Louisville, KY; and Knoxville, TN. Ammonium sulfate concentrations are comparable between urban and nearby Class I areas, while organic carbon, elemental carbon, and nitrate concentration are generally higher in urban areas than the Class I areas. These results suggest that sulfate is widely distributed regionally while urban areas

see an additiona l incremental pollutant loading from local emissions sources.

Role of meteorology in determining visibility conditions: Classification and Regression Tree Analyses were used to characterize the relationship between meteorological conditions and

visibil ity conditions at the Class I areas. Days were assigned to one of five visibility classes ranging from poor to good visibility. Days were then assigned to bins based on meteorological

conditions. For the central and northern Florida Class I areas poor v isibility days were most15 likely to occur on days with high temperatures, high relative humidity, low wind speeds, where previous days were also with poorer visibility

. That is poor visibility is usually a multi

- day event with building pollution levels. P recipitation was not a good predictor of visibility condition. In South Florida (Everglades), poor visibility is not differentiated by high temperatures and relative humidity and is more related to precipitation and number of rain events. Weights were as signed to days based on frequency of occurrence of days with similar meteorological conditions.

The above analyses are further discussed in Appendix B. Also, the pollutant

- specific extinction for each of the best and worst 20 percent days are provided g raphically in Appendix E, sub

Appendix E.3.



### 3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064

As stated in section 1.3 above, the RHR directs states to graphically show what would be a “uniform rate of progress” toward natural conditions for each mandato ry Class I Federal area within the State as well as for each mandatory Class I Federal area located outside the State, which may be affected by emissions from sources within the State. The uniform rate of progress is also known as the “glidepath.” The gli depath is simply a straight graphical line drawn from the baseline level of visibility impairment for 2000

- 2004 to the level representing no manmade impairment in 2064.

Each state must set goals for each Class I area that provide for reasonable progress towards achieving natural visibility conditions by 2064. Section 51.308(d)(1) of the regional haze rule requires that reasonable progress goals must both: (1) provide f or improvement in visibility for the most impaired days over the period of the implementation plan; and (2) ensure no degradation in visibility for the least impaired days over the same period.

Uniform rate of progress graphs (glidepaths), were develop ed for each Class I area in the VISTAS region. The glidepaths were developed in accordance with USEPA guidance and used data collected from the IMPROVE monitoring sites as described in section 2 above. The glidepath is one of the indicators used in setti ng reasonable progress goals.

3.1 Glidepaths for Class I Areas in Florida and Nearby States

The following are glidepaths for the 20 percent most impaired days for Everglades National Park, Chassahowitzka National Wilderness Area, St. Marks National Wilde rness Area,

Okefenokee National Wilderness Area, and Breton National Wilderness Area , assuming uniform rate of progress toward regional haze goals. Wolf Island National Wilderness Area does not

have an IMPROVE monitor and the Okefenokee site acts as its surrogate

. Natural background visibility at all five sites is calculated to be about 11 to 12 dv. In general, these five Class I areas have better visibility conditions than in other areas of the Southeast. Of these five areas,

Okefenokee has the worst current visibility and has greatest improvement to make to achieve natural visibility conditions (Table 3.1.1). The Everglades has the best current visibility16 conditions in the entire Southeast, and has the least amount of needed improvement to achieve

na tural conditions.

Class I Area State Baseline

- Avg. of 20% Worst Days (2000

- 2004) (dv) Natural Background

Goal (dv) Necessary Improvement in Visibility to Attain Goal (dv) Everglades FL 22.30 12.09 10.21 Swanquarter NC 24.71 11.55 13.17
Chassahowitzka FL 25.75 11.03 14.71

Breton LA 26.00 11.93 14.07 Upper Buffalo AR 26.27 11.49 14.78
Saint Marks FL 26.31 11.67 14.64 Caney Creek AR 26.36 11.57 14.79 Cape Romain SC 26.48 12.10 14.38

Hercules

- Glade MO 26.75 11.24 15.50
Okefenokee GA 27.13 11.21 15.93 Mingo MO 28.05 11.62 16.43 Shining Rock NC 28.66 11.47 17.18 Linville Gorge NC 28.77 11.19 17.59 Brigantine NJ 29.01 12.06 16.95 Sipsey AL 29.03 10.90 18.12 Dolly Sods WV 29.04 10.36 18.68

James River Face VA 29.12 11.09 18.02
Shenandoah VA 29.31 11.37 17.94
Cadiz KY 29.39 10.82 18.58
Cohutta GA 30.25 10.78 19.48 Great Smoky Mountains TN 30.28 11.05 19.22
Mammoth Cave KY 31.37 11.08 20.29

Table 3.1.1

– Rank Order of Current Visibility for Southeastern Class I Areas17 Figure 3.1

- 1. Uniform Rate of Progress Glidepath for 20 percent worst days at Everglades

National Park. 

### Uniform Rate of Progress Glide Path Everglades

- W20% Data Days

22.30 21.45
19.92

18.22

16.52

14.82

13.11 12.09

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation18 Figure 3.1

- 2. Uniform Rate of Progress Glidepath for 20 percent worst days

Chassahowitzka National Wilderness Area. 

### Uniform Rate of Progress Glide Path

Chassahowitzka

- W20% Data Days

25.75 24.52

22.31

19.86

17.41

14.96

12.50 11.03

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation19 Figure 3.1

- 3. Uniform Rate of Progress Glidepath for 20 percent worst days at St. Marks

National Wilderness Area. 

### Uniform Rate of Progress Glide Path

Saint Marks

- W20% Data Days

26.31 25.09

22.89

20.45

18.01

15.57

13.13 11.67

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation20 Figure 3.1

- 4. Uniform Rate of Progress Glidepath for 20 percent worst days at Okefenokee

National Wilderness Area. 

### Uniform Rate of Progress Glide Path

Oke fenokee

- W20% Data Days

27.13 25.81

23.42

20.76

18.11

15.45

12.80
11.21

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation21 Figure 3.1

- 5. Uniform Rate of Progress Glidepath for 20 percen t worst days at Breton

National Wild erness Area

.



### 4.0 NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY

IMPAIRMENT IN FLORIDA CLASS I AREAS

4.1 Baseline Emissions Inventory

The Regional Haze Rule at 51.308(d) (4) (v) requires a statewide emissions inventory of pollutants that are reasonably anticipated to cause or contribute to visibility impairment in any mandatory Class I area. An inventory was developed for the baseline year 2002 and projected to 2009 and 2018. The pollutants inventoried include volatile organic compounds, nitrogen oxides, fine particulate (PM2.5), coarse particulate (PM10), ammonia and sulfur dioxide. The baseline emissions inventory for 2002 was deve loped for Florida and the other VISTAS states following the methods described in Appendix D.

There are five different emission inventory source classifications: stationary point and area sources, off

- road and on

- road mobile sources, and biogenic source s. Stationary point sources are those sources that emit greater than a specified tonnage per year, with data provided at the facility level. Electric generating utilities and industrial sources are the major categories for stationary point sources. Stat ionary area sources are those sources whose individual emissions 

### Uniform Rate of Progress Glide Path

Breton

- W20% Data Days

26.00 24.82

22.71

20.37

18.02

15.68

13.34 11.93

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation22 are relatively small, but due to the large number of these sources, the collective emissions from the source category could be significant (i.e., dry cleaners, service stations, agricultural sources, fire emissions, etc.). These types of emissions are estimated on a countywide level. Non

- road (or off

- road) mobile sources are equipment that can move but do not use the roadways, i.e., lawn mowers, construction equipment, railroad locomotives, aircraft, etc. The emissions from these sources, like stationary area sources, are estimated on a countywide level. On

- road mobile sources are automobiles, trucks, and motorcycles that use the roadway system. The emissions

from these sources are estimat ed by vehicle type and road type and are summed to the countywide level. Biogenic sources are the natural sources like trees, crops, grasses and natural decay of plants. The emissions from these sources are estimated on a countywide level.

In addition t o the various source classifications, there are also various types of emission inventories. The first is the actual base year inventory. This inventory is the base year emissions that correspond to the meteorological data used, which for this modeling ef fort is data from 2002. These emissions are used for evaluating the air quality model performance.

The second type of inventory is the typical base year inventory. This inventory is similar to the actual base year inventory, except that for sources who se emissions change signficantly from year to year, a more typical emission value is used. In this modeling effort, typical emissions were developed for the electric generating units (EGUs) and the wildland fire emissions. The air quality modeling runs u sing the typical base year inventory provide results which are then used to calculate relative reduction factors for future years. These relative reduction factors for future years are then used to demonstrate reasonable progress toward visibility goals.

Below is an overview of the inventories used for each source classification. More detailed discussion of the emissions inventory development is contained in Appendix D.

4.1.1 Stationary Point Sources

Point source emissions are emissions from individua l sources having a fixed location. Generally, these sources must have permits to operate, and their emissions are inventoried on a regular schedule. Large sources emitting at least 100 tons per year (tpy) of a criteria pollutant, 10 tpy of a single hazar dous air pollutant (HAP), or 25 tpy total HAP are inventoried annually. Smaller sources have been inventoried less frequently. The point source emissions data can be grouped as EGU sources and other industrial point sources, also called non

- EGUs.

The actual base year inventory for the EGU sources used 2002 continuous emissions monitoring (CEM) data reported to the USEPA’s Acid Rain program or 2002 hourly emissions data provided by stakeholders. These data provide hourly emission s profiles for SO 2 and NOx that can be used in air quality modeling. Emissions profiles are used to estimate emissions of other pollutants (volatile organic compounds, carbon monoxide, ammonia, fine particles, soil) based

on measured emissions of SO 2 and NOx. Electri c Generating Units23 Emissions from EGU vary daily and seasonally as a function of variability in energy demand and utilization and outage schedules. To avoid anomalies in future year emissions created by relying on 2002 operations to represent future operations, a t ypical base year emissions inventory was developed for EGUs. This approach is consistent with the USEPA’s modeling guidance. To develop a typical year 2002 emissions inventory for EGU sources, each unit’s average CEM heat input for 2000 through 2004 was divided by the 2002 actual heat input to generate a unit specific normalizing factor. This normalizing factor was then multiplied by the 2002 actual emissions. The heat inputs for the period 2000 through 2004 were used because the modeling current design

values use monitored data from this same 5

- year period. If a unit was shut down for an entire year during the 2000 through 2004 period, the average of the years the unit was operational was used. If a unit was shut down in 2002, but not permanently shut down, the emissions and heat inputs from 2001 (or 2000) were used in the normalizing calculations.

As part of the VISTAS air quality modeling, VISTAS, in cooperation with the other eastern RPOs, contracted with ICF Resources, L.L.C., to generate future y ear emission inventories for the electric generating sector of the contiguous United States using the Integrated Planning Model ( IPM) . IPM is a dynamic linear optimization model that can be used to examine air pollution control policies for various pollut ants throughout the contiguous United States for the entire electric power system. The dynamic nature of IPM enables projection of the behavior of the power system over a specified future period. Optimization logic in IPM determines the least

cost means of meeting electric generation and capacity requirements while complying with specified constraints including air pollution regulations, transmission bottlenecks, and plant

specific operational constraints. The versatility of IPM allows users to specify w hich constraints to exercise, and to populate IPM with their own datasets.

For the non

- EGU sources, the same inventory is used for both the actual and typical base year

emissions inventories. The non

- EGU category uses annual emissions as reported under the Consolidated Emissions Reporting Rule (CERR) for the year 2002. These emissions are temporally allocated to month, day, and hour using source category code ( SCC )

- based allocation

factors. Other Industrial Point Sources

The general approach for assembling future year data was to use recently updated growth and

control data consistent with USEPA’s CAIR analyses. This data was supplemented with state

specific growth factors and stakeholder input on growth assumptions.

4.1.2 Stationary Area Source s

Stationary area sources are sources whose individual emissions are relatively small, but due to the large number of these sources, the collective emissions could be significant (i.e., combustion of fuels for heating, structure fires, service stations, et c.). Emissions are estimated by multiplying an emission factor by some known indicator of collective activity, such as fuel usage, number of households, or population. Stationary area source emissions are estimated at the countywide level.24 The actual ba se year inventory will serve as the typical base year inventory for all area source categories except for wildland fires. For wildland fires, a typical year inventory was used to avoid anomalies in wildfire activity in 2002 compared to longer term average s. Development of a typical year fire inventory provided the capability of using a comparable data set for both the base year and future years. Thus, fire emissions remain the same for air quality modeling in both the base and any future years. The VIST AS Fire Special Interest Work Group used State records to ratio the number of acres burned over a longer term period (three or more years, as available from state records) to 2002. Based on these ratios, the 2002 acreage was then scaled up or down

to deve lop a typical year inventory.

Future year emissions The VISTAS contractor generated future year emissions inventories for 2009 and 2018 for the regional haze modeling. Growth factors, supplied either by states or taken from the CAIR emission projection s, were applied to project the controlled emissions to 2018. If no growth factor was available from either a state or the CAIR growth factor files, then the USEPA’s Economic Growth and Analysis System Version 5 growth factors were used.

4.1.3 Off

- Road Mo bile Sources

Off

- road (or non

- road) mobile sources are equipment that can move but do not use the roadways, such as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, etc.

For t he majority of the non

- road mobile sources, th e emissions for 2002 were estimated using the USEPA’s NONROAD2005c model. For the three source categories not included in the NONROAD model, i.e., aircraft engines, railroad locomotives and commercial marine, more traditional methods of estimating the emi ssions were used. The same inventory is used for both the actual and typical base year emissions inventories.

For the source categories estimated using the USEPA’s NONROAD model, the model growth assumptions were used to create the 2009 and 2018 future year inventories. The NONROAD model takes into consideration regulations affecting emissions from these source categories. For the four largest airports in North Carolina, the Federal Aviation Adminstration’s Terminal Area Forecast was used to project g rowth in aircraft emissions. For the commercial marine, railroad locomotives and the remaining airport emissions, the VISTAS contractor calculated the future growth in emissions using detailed inventory data (both before and after controls) for 1996 and

2 010, obtained from the CAIR Technical Support Document. When available, state

- specific growth factors were used.

4.1.4 Highway Mobile Sources

For onroad vehicles, the newest version of the MOBILE model, MOBILE6.2, was used. Key inputs for MOBILE includ e information on the age of vehicles on the roads, the average speeds on the roads, the mix of vehicles on the roads, any programs in place in an area to reduce emissions for motor vehicles (e.g., emissions inspection programs), and temperature.25 The MOBIL E model takes into consideration regulations that affect emissions from this source sector. The same MOBILE run is used to represent the actual and typical year emissions for onroad vehicles using input data reflective of 2002. The MOBILE model then is r un for 2018 inventory using input data reflective of that year. The 2002 vehicle miles traveled (VMT), speeds, vehicle age and vehicle mix data were obtained from the Florida Department of Transportation (FLDOT). For urban areas in Florida that run trave l demand models (TDMs), VMT and speed data from TDMs were used.

4.1.5 Biogenic Emission Sources

Biogenic emissions were prepared with the SMOKE

- BEIS3 (Biogenic Emission Inventory System 3 version 0.9) preprocessor. SMOKE

- BEIS3 is a modified version of th e Urban Airshed Model (UAM)

- BEIS3 model. Modifications include use of MM5 data, gridded land use data, and improved emissions characterization. The emission factors that are used in SMOKE

- BEIS3

are the same as the emission factors as in UAM

- BEIS3. The b asis for the gridded land use data used by BEIS3 is the county land use data in the Biogenic Emissions Landcover Database version 3 (BELD3) provided by the USEPA. A separate land classification scheme, based upon satellite (AVHRR, 1 km spatial resolution) and census information, aided in defining the forest, agriculture and urban portions of each county.

4.1.6 Summary 2002 Base G2 (February 2007 version) Baseline Emissions Inventory for FL

Table 4.1 is a summary of the 2002 baseline emission inventory for Florida. The complete inventory and discussion of the methodology is contained in Appendix D. The emissions summaries for other VISTAS states can also be found in Appendix D.

Table 4.1. 2002 Emissions Inventory Summary for FL in tons per year.

CO NH 3 NOx PM10 PM2.5 SO 2 VOC

EGU 57,113 234 257,677 21,387 15,643 453,631 2,524

NON

- EGU 81,933 1,423 45,156 35,857 30,504 65,090 38,471

ONROAD 4,493,820 17,922 460,503 11,148 7,779 20,687 520,757

NON

- ROAD 1,920,729 134 180,627 18,281 17,415 20,614 272,072

AREA 71,079 37,446 28,872 443,346 58,878 40,491 404,302

FIRES 790,620 3,102 15,942 85,263 75,717 4,057 42,724

BIOGENICS 0 0 36,320 0 0 0 1,522,031

TOTAL 7,415,293 60,261 1,025,098 615,281 205,936 604,570 2,802,880

4.1.7 Model Performance Improvements through Emissions Inventory Improvements

Since the initial model performance evaluation, VISTAS has made several improvements to the emissions inventory, which in turn improve model performance. These inventory improvements are detailed in the VISTAS emissions inventory report and Appendix D, an d are summarized

here:

**•** For electric generating utilities, the Integrated Planning Model (IPM) was used to provide estimates of future year utility production and emissions. Continuous Emissions Monitoring data was used to define seasonal variability in production and emissions . For26 Base G2 emissions, states updated IPM model projections from 2005 with control data provided by utility companies in 2006 through winter 2007.

**•** For on

- road vehicle emissions, states and local agencies provided updated MOBILE model input and vehicle

- miles

- traveled data.

**•** For ammonia emissions from agricultural sources, the Carnegie Mellon University ammonia model was used to improve annual and monthly estimates.

**•** For fires, the VISTAS states provided fire activity data for 2002 for wildfires, prescribed fire, land clearing and agricultural burning and MACTEC developed a 2002 fire inventory. Where data allowed, Alpine Geophysics modeled fire events as point sources. In 2006, United States Forest Service and Fish an d Wildlife Service provided projection s of increased prescribed burning in 2009 and 2018; these data were incorporated in the Base G inventory for all states except Florida.

**•** For non

- road engines, the updated USEPA NONROAD2005 emissions model was used

in Base G.

**•** For commercial marine emissions in shipping lanes in the Gulf of Mexico and Atlantic Oceans, ENVIRON created gridded emissions for the VISTAS modeling domain using inventory data newly developed for USEPA by Corbett at University of Delaware. These emissions were incorporated in the Ba se G modeling.

**•** Updated inventories from the neighboring RPOs, Mexico, and Canada were incorporated

as available.

4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories

Ammonium sulfate is the largest contributor to vi sibility impairment at Florida Class I areas, and

reduction of SO 2 emissions would be the most effective means of reducing ammonium sulfate.

With the lower SO 2 emissions in South Florida, and its greater distance from the large

continental emissions of SO 2 , th e Everglades National Park is occasionally dominated by carbon. As illustrated in Figure 4.2

- 1, 96 percent of SO 2 emissions in the VISTAS states are attributable to electric generating facilities and industrial point sources. As shown in Table 4.1, 92 percent of

SO 2 emissions in Florida are attributable to electric generating facilities and industrial point sources (electric generating facilities, non

- electric generating facilities, and area sources).



## VISTAS States SO2 Emissions

- 2002

Utilities Industrial On road
Non road Other Utilities: 76% Industrial: 20% 4.8 Million tons



## VISTAS States SO2 Emissions

- 2002

Utilities Industrial On road
Non road Other Utilities: 76% Industrial: 20% 4.8 Million tons27 Figure 4.2

- 1. SO 2 emissions in 2002 in the VIST AS States.



### 5.0 REGIONAL HAZE MODELING METHODS AND INPUTS

Modeling for regional haze was performed by VISTAS for the ten southeastern states, including

Florida . The sections below outline the methods and inputs used by VISTAS for the regional modeling. A dditional details are provided in Appendices C.

5.1 Analysis Method

The modeling analysis is a complex technical evaluation that begins by selection of the modeling system. VISTAS decided to use the following modeling system:

**•** Meteorological Model: The Pe nnsylvania State University/National Center for Atmospheric Research ( PSU/NCAR ) Mesoscale Meteorological Model ( MM5 ) is a nonhydrostatic, prognostic meteorological model routinely used for urban

- and regional

scale photochemical, fine particulate matter, a nd regional haze regulatory modeling

studies.

**•** Emissions Model: The Sparse Matrix Operator Kernel Emissions ( SMOKE ) modeling system is an emissions modeling system that generates hourly gridded speciated emission inputs of mobile, nonroad mobile, area, poin t, fire and biogenic emission sources for photochemical grid models.

**•** Air Quality Model: USEPA’s Models

- 3/ Community Multiscale Air Quality (CMAQ) modeling system is an ‘One

- Atmosphere’ photochemical grid model capable of addressing ozone, particulate matter (PM), visibility and acid deposition at regional scale for periods up to one year.

The USEPA Modeling Guidance recommends modeling an entire year or at a minimum several days in each quarter of a year to adequately represent the range of meteorologi cal conditions that contribute to elevated levels of fine particulate matter. The year 2002 was selected by VISTAS as the modeling year for this demonstration. Meteorological inputs were developed for 2002 using the meteorological model. Emission invent ories were also developed for 2002 and processed through the emissions model. These inputs were used in the air quality model to predict fine particle mass and visibility. The model results for 2002 were compared with observed meteorological and air qual ity data to evaluate model performance. Several configurations of the meteorological and air quality model were evaluated to select a configuration that gave the best overall performance for the VISTAS region.

Once model performance was deemed adequate, the current and future year emissions were processed through the emissions model. The air quality modeling results are used to determine a relative reduction in future visibility impairment, which is used to determine reasonable progress.

The complete m odeling protocol used for this analysis can be found in Appendix C.28 5.2 Model Selection

To ensure that a modeling study is defensible, care must be taken in the selection of the models to be used. The models selected must be scientifically appropriate fo r the intended application and be freely accessible to all stakeholders. Scientifically appropriate means that the models address important physical and chemical phenomena in sufficient detail, using peer

- reviewed methods. Freely accessible means that mo del formulations and coding are freely available for review and that the models are available to stakeholders, and their consultants, for execution and

verification at no or low cost.

The following sections outline the criteria for selecting a modeling sy stem that is both defensible and capable of meeting the study's goals. These criteria were used in selecting the modeling system used for this modeling demonstration.

5.2.1 Selection of Photochemical Grid Model

For a photochemical grid model to qualify as a candidate for use in a regional haze SIP, a State needs to show that it meets the same several general criteria as a model for an attainment demonstration for a national ambient air quality standard (NAAQS): Criteria

The model has received a scientif ic peer review

The model can be demonstrated applicable to the problem on a theoretical basis

Data bases needed to perform the analysis are available and adequate

Available past appropriate performance evaluations have shown the model is not biased

toward underestimates or overestimates

A protocol on methods and procedures to be followed has been established

The developer of the model must be willing to make the source code available to users for free or for a reasonable cost, and the model cannot otherwise be proprietary.

The photochemical model selected for this study was CMAQ version 4.5. For more than a decade, the USEPA has been developing the Models

- 3 CMAQ modeling system with the overarching aim of producing a ‘One

- Atmosphere’ air qu ality modeling system capable of addressing ozone, fine particulate matter, visibility and acid deposition within a common platform. The original justification for the Models

- 3 development emerged from the challenges posed by the 1990 CAAA and the USEPA’s desire to develop an advanced modeling framework for ‘holistic’ environmental modeling utilizing state

- of

- science representations of atmospheric processes in a high performance computing environment. The USEPA completed the initial stage of development w ith Models

- 3 and released the CMAQ model in mid

- 1999 as the initial operating science model under the Models

- 3 framework. The most recent rendition is CMAQ version 4.5, which was released in September 2005. Overview of CMAQ29 An advantage of choosing CMAQ as the atmosphe ric model is the ability to do one

- atmospheric modeling. The same model configuration is being applied for the ozone and PM 2.5 attainment demonstrations SIPs, as well as the regional haze SIP. A number of features in CMAQ’s

theoretical formulation and te chnical implementation make the model well suited for annual PM modeling.

The configuration used for this modeling demonstration, as well as a more detailed description of the CMAQ model, can be found in the Modeling Protocol (Appendix C).

5.2.2 Selection of Meteorological Model

Meteorological models, either through objective, diagnostic, or prognostic analysis, extend available information about the state of the atmosphere to the grid upon which photochemical grid modeling is to be car ried out. The criteria for selecting a meteorological model are based on both the models ability to accurately replicate important meteorological phenomena in the region of study, and the model's ability to interface with the rest of the modeling systems

-- particularly the photochemical grid model. With these issues in mind, the following criteria were established for the meteorological model to be used in this study: Criteria

Non

- Hydrostatic Formulation

Reasonably current, peer reviewed formulation

Simulates Clo ud Physics

Publicly available on no or low cost

Output available in I/O API format

Supports Four Dimensional Data Assimilation (FDDA)

Enhanced treatment of Planetary Boundary Layer heights for AQ modeling

The non

- hydrostatic MM5 model is a three

- dimensional, limited

- area, primitive equation, prognostic model that has been used widely in regional air quality model applications. The basic model has been under continuous development, improvement, testing and open peer

- review for

more than 20 years and has been used worldwide by hundreds of scientists for a variety of

mesoscale studies. Overview of MM5

MM5 uses a terrain

- following non

- dimensionalized pressure, or "sigma", vertical coordinate similar to that used in many operational and research models. In the non

- hydrostatic MM5, the sigma levels are defined according to the initial hydrostatically

- balanced reference state so that the sigma levels are also time

- invariant. The gridded meteorological fields produced by MM5 are directly compatible with the i nput requirements of ‘one atmosphere’ air

- quality models using this coordinate. MM5 fields can be easily used in other regional air quality models with different30 coordinate systems by performing a vertical interpolation, followed by a mass

- conservation re

adjustment.

Distinct planetary boundary layer ( PBL ) parameterizations are available for air

- quality applications, both of which represent sub

- grid

- scale turbulent fluxes of heat, moisture and

momentum. One scheme uses a first

- order eddy diffusivity for mulation for stable and neutral

environments and a modified first

- order scheme for unstable regimes. The other scheme uses a prognostic equation for the second

- order turbulent kinetic energy, while diagnosing the other key boundary layer terms.

Initial and lateral boundary conditions are specified for real

- data cases from mesoscale three

dimensional analyses performed at 12

- hour intervals on the outermost grid mesh selected by the user. Surface fields are analyzed at three

- hour intervals. A Cressman

- b ased technique is used to analyze standard surface and radiosonde observations, using the National Meteorological Center's spectral analysis, as a first guess. The lateral boundary data are introduced using a relaxation technique applied in the outermost f ive rows and columns of the coarsest grid domain.

MM5 modeling system in regulatory air quality application studies have been widely reported in the literature (e.g., Emery et al., 1999; Tesche et al., 2000, 2003) and many have involved comparisons with o ther prognostic models such as the Regional Atmospheric Modeling System ( RAMS ) and the Systems Application International Mesoscale Model. The MM5 enjoys a far richer application history in regulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 recent regional scale a ir quality application studies since 1995, it has generally been found that the MM5 model tends to produce somewhat better photochemical model inputs than alternative models.

The configuration used for this modeling demonstration, as well as a more deta iled description of the MM5 model, can be found in the meteorological modeling protocol (Append icies E and F2 ).

5.2.3 Selection of Emissions Processing System

The principal criterion for an emissions processing system is that it accurately prepares emissions files in a format suitable for the photochemical grid model being used. The following

list includes clarification of this criterion and additional desirable criteria for effective use of the

system. Criteria

File System Compatibility with the I /O API

File Portability

Ability to grid emissions on a Lambert Conformal projection

Report Capability

Graphical Analysis Capability

MOBILE6 Mobile Source Emissions31 Biogenic Emissions Inventory System version 3 (BEIS

- 3)

Ability to process emissions for the proposed domain in a reasonable amount of time.

Ability to process control strategies

No or low cost for acquisition and maintenance

Expandable to support other species and mechanisms

The SMOKE Emissions Processing System Prototype was or iginally developed at the Micro

computing Center of North Carolina. As with most ‘emissions models’, SMOKE is principally

an emission processing system and not a true emissions modeling system in which emissions estimates are simulated from ‘first princip les’. This means that, with the exception of mobile and biogenic sources, its purpose is to provide an efficient, modern tool for converting emissions inventory data into the formatted emission files required by an air quality simulation model. For

mobil e sources, SMOKE actually simulates emissions rates based on input mobile

- source activity data, emission factors and outputs from transportation travel

- demand models. Overview of SMOKE

SMOKE was originally designed to allow emissions data processing methods to utilize e mergent high

- performance

- computing as applied to sparse

- matrix algorithms. Indeed, SMOKE is the fastest emissions processing tool currently available to the air quality modeling community. The sparse matrix approach utilized throughout SMOKE permits both rapid and flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series of matrix calculations instead of less efficient algorithms used in previous systems. The processing is flexible because the processing steps of tem poral projection, controls, chemical speciation, temporal allocation, and spatial allocation have been separated into independent operations wherever possible. The results from these steps are merged together at a final stage of processing.

SMOKE contai ns a number of major features that make it an attractive component of the modeling system. The model supports a variety of input formats from other emissions processing systems and models. It supports both gridded and county total land use scheme for

bio genic emissions modeling. SMOKE can accommodate emissions files from up to 10 countries and any pollutant can be processed by the system. For additional information about the SMOKE model please refer to Modeling Protocol (Appendix C).

5.3 Selection of th e Modeling Year

A crucial step to SIP modeling is the selection of the period of time to model to represent current air quality conditions and to project changes in air quality in response to changes in emissions. The year 2002 was selected as the base ye ar for several reasons.

The USEPA’s April 2007 Guidance on the use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze identifies specific goals32 to consider when selecting one or more episodes for use in demonstrating reasonable progress in attaining the regional haze air quality goals. The USEPA recommends that episode selection derive from three principal criteria:

**•** Simulate a variety of meteorological conditions;

**•** Model time periods in which observed concentrations are close to the appropriate baseline design value or visibility impairment;

**•** Model periods for which extensive air quality/meteorological data bases exist; and

**•** Model a sufficient number of days so that the modeled attainme nt test applied at each monitor violating the NAAQS is based on multiple days.

For regional haze modeling, the guidance goes further by suggesting that the preferred approach is to model a full, representative year. Moreover, the required RRF values shou ld be based on model results averaged over the 20% worst and 20% best visibility days determined for each Class I are based on monitoring data from the 2000

– 2004 baseline period.

The USEPA also lists several other considerations to bear in mind when c hoosing potential regional haze episodes including: (a) choose periods which have already been modeled, (b) choose periods which are drawn from the years upon which the current design values are based, (c) include weekend days among those chosen, and (d) c hoose modeling periods that meet as many episode selection criteria as possible in the maximum number of nonattainment or Class I areas as possible. Finally, the USEPA explicitly recommended in its 2007 guidance to use 2002 as the baseline inventory year.

VISTAS adopted a logical, stepwise approach in implementing the USEPA guidance in order to identify the most preferable, representative year for regional haze modeling. These steps include the following:

Representativeness of Meteorological Conditions: The VISTAS meteorological contractor (BAMS) identified important meteorological characteristics and data sets in the VISTAS region directly relevant to the evaluation of candidate annual modeling episodes.

Initial Episode Typing: At the time of selectio n in 2003, meteorological and air quality data were available for 2002 for model inputs and model performance evaluation. VISTAS used Classification and Regression Tree Analyses to evaluate visibility conditions for 2000, 2001, and 2002, the candidate mod eling years. The year 2002 was found to be representative of conditions in the other two years. Subsequently, these analyses were repeated with the meteorological and air quality monitoring data for 2000 to 2004 to evaluate how well the 2002 modeling yea r represented the full 2000

- 2004 baseline period. This analysis confirmed that visbility and PM2.5 mass in 2002 were representative of the five

- year baseline period for the VISTAS Class I areas. This analysis is discussed in more detail in the project re port in Appendix B.33 Data Availability: In parallel with the CART analysis, episode characterization analyses, collaborative investigations by VISTAS states (e.g., NCDAQ, Georgia DNR, FL DEP) intensively studied the availability of PM 2.5 , meteorological, a nd emissions data and representativeness of alternative Baseline modeling periods from a regulatory standpoint. Additionally, 2002 was the year that USEPA was requiring states to provide emissions inventory data for the Comprehensive Emissions Reporting R ule, it made sense to use 2002 as the modeling year to take advantage of the 2002 inventory. Years to be used by other RPOs: VISTAS also considered what years other RPO would be modeling, and several had already chosen calendar year 2002 as the modeling ye ar.

After a lengthy process of integrated studies, the episode selection process culminated in the selection of calendar year 2002 (1 January through 31 December) as the most current, representative, and pragmatic choice for VISTAS regional haze modeling. All of the USEPA criteria for regional haze episode selection were directly considered in this process together with many other considerations (e.g., timing of new emissions or aerometric data deliveries by the USEPA or the states to the modeling teams).

5.4 Modeling Domains

5.4.1 Horizontal Modeling Domain

The USEPA’s modeling guidance recommends a 12

- km modeling grid resolution for PM2.5 modeling while a 36

- km grid is considered acceptable for regional haze. For the VISTAS modeling, a coarse 36

- km grid resolution was used for modeling the entire United Sta tes and a

finer 12

- km grid was used to model the eastern United States.

The CMAQ model was run in one

- way nested grid mode. This allowed the larger outer domains

to feed concentration data to the inner nested domain. One

- way nesting is believed to be

a ppropriate for the generally stagnant conditions experienced during North Carolina ozone episodes. Two

- way nesting was not considered due to numerical and computational uncertainty associated with the technique.

The horizontal coarse grid modeling domain boundaries were determined through a national effort to develop a common grid projection and boundary. A smaller 12

- km grid, modeling domain was selected in an attempt to balance location of areas of interest, such as ozone and fine particulate matter no nattainment areas, as well as Class 1 and wilderness areas for regional haze. Processing time was also a factor in choosing a smaller 12

- km grid, modeling domain.

The coarse 36

- km horizontal grid domain covers the continental United States. This domain was used as the outer grid domain for MM5 modeling with the CMAQ domain nested within the MM5 domain. Figure 5.4.1

- 1 shows the MM5 horizontal domain as the outer most, blue grid with the CMAQ 36

- km domain nested in the MM5 domain.34 Figure 5.4.1

- 1. The MM5 horizontal domain is the outer most, blue grid, with the CMAQ

36

- km domain nested in the MM5 domain.

To achieve finer spatial resolution in the VISTAS states, a one

- way nested high resolution (12

- km grid resolution) was used. Figure 5.4.1

- 2 shows t he 12

- km grid, modeling domain for the VISTAS region. This is the modeling domain for which the reasonable progress goals will

be assessed.

Figure 5.4.1

- 2. A more detailed view of the 12

- km grid over the VISTAS region.35 5.4.2 Vertical Modeling Domain

Th e CMAQ vertical structure is primarily defined by the vertical grid used in the MM5 modeling. The MM5 model employed a terrain following coordinate system defined by pressure, using 34 layers that extend from the surface to the 100 mb. A layer

- averaging scheme was used to generate 19 vertical layers for CMAQ to reduce the computational cost of the CMAQ simulations. The effects of layer averaging were evaluated in conjunction with the VISTAS modeling effort and was found to have a relatively minor effect on the model performance metrics when both the 34 layer and a 19 layer CMAQ models were compared to ambient monitoring data.



### 6.0 MODEL PERFORMANCE EVALUATION

The initial modeling effort focused on evaluating previous regional air quality modeling applica tions and testing candidate model configurations for the SMOKE emissions and CMAQ

model for the VISTAS 36

- km and 12

- km modeling domains. This effort resulted in a report recommending the model configuration for the annual emissions and air quality modelin g, which is included as part of the VISTAS Emissions and Air Quality Modeling Protocol. The evaluation of the meteorological modeling configuration can be found in Appendix F.1, with a summary of the final meteorological and air quality modeling configura tion in the modeling protocol contained in Appendix E and Appendix C, respectively.

Air quality model performance for the 2002 modeling year was initially tested in 2004 using an early version of the VISTAS emissions inventory. In keeping with the one

- at mosphere objective of the CMAQ modeling platform, model performance was evaluated based on measured ozone, fine particles, and acid deposition in the Air Quality System (AQS), IMPROVE, Speciated Trends Network (STN), Southeastern Aerosol Research and Chara cterization (SEARCH), National Acid Deposition Program (NADP) and Clean Air Status and Trends Network (CASTNet) monitoring networks (Figure 6.0

- 1).36 Figure 6.0

- 1: Monitoring Networks used for VISTAS 2002 model performance evaluation

and their locati on within the VISTAS 12km domain.37 6.1 Modeling Performance Goals, and Criteria

In 2004, VISTAS established model performance goals and criteria for components of fine particle mass (Table 6.1

- 1) based on previous model performance for ozone and fine particles. The USEPA modeling guidance for fine particulate matter at the time noted that PM models might not be able to achieve the same level of performance as ozone models. VISTAS’ evaluation considered several statistical performance measures and displays. Fractional bias and mean fractional error were selected as the most appropriat e metrics to summarize model performance; other metrics were also calculated and are included for IMPROVE monitors in the full model performance evaluation (Appendix F.2).

Table 6.1

- 1: Established model performance goals and criteria for the component species of fine particle mass.

Fractional Bias Mean Fractional

Error Comment

< 15 percent < 35 percent Goal for PM model performance based on ozone model performance, considered excellent performance

< 30 percent < 50 percent Goal for PM model performance, considered good performance

< 60 percent < 75 percent Criteria for PM model performance, considered average performance. Exceeding this level of performance indicates fundamental concerns with the modeling system and triggers diagnostic evaluation.

Several graphic displays of model performance were prepared including:

1. Scatter plots of predicted and observed concentrations and deposition by species, monitoring network, and month

2. Time series plots of predicted and obs erved concentrations and deposition by species, monitoring site, and month

3. Spatially average time series plots

4. Time series plots of monthly fractional bias and error for a species, region, and network

5. Performance goal plots (“soccer plots”) that summarize model performance by species, region, season

6. Concentration performance plots (“bugle plots”) that display fractional bias or error as a function of concentration by species, region, monitoring network, and month

The “soccer plots” and “bugle plots” are r elatively new tools in model performance evaluations, and have recently been included as model performance evaluation displays in the USEPA’s modeling guidance for Ozone, PM2.5, and Regional Haze (2007). Both “soccer plots” and “bugle plots” allow for con venient way to examine model performance with respect to set goals and criteria. The bugle plots have the added benefit of adjusting the goals and criteria to consider the concentration of the species. Analysis of “bugle plots” generally suggests that

gr eater emphasis should be placed on performance of those components with the greatest contribution to PM mass and visibility impairment (e.g. sulfate and organic carbon) and that greater bias and error could be accepted for components with smaller contribut ions to total PM mass (e.g. elemental carbon, nitrate, and soil).38 6.2 VISTAS Domain

- Wide Performance

Further discussion of model performance in this document will focus on the comparison of observational data from the IMPROVE monitors and model output data from the 2002 VISTAS

BaseG2

- Actual annual air quality modeling. Focus is limited to the IMPROVE mon itoring network as these sites are the locations used in projecting attainment visibility improvement goals in the Class I areas.

The evaluation will primarily focus on the air quality model’s performance with respect to individual components of fine pa rticulate matter (PM 2.5 ), as good model performance of the component species will dictate good model performance of total or reconstituted fine particulate matter. Model performance of the total fine particulate matter and the resulting total light

extinc tion will also be provided as a means to discuss the overall model performance for this Implementation Plan.

In our analyses, mean fractional bias (error) is used in lieu of mean bias (error), to prevent low observations and model predictions from skewin g the metrics. A full list of model performance statistics is found in Appendix F.2. The soccer and bugle plots for the all of the VISTAS IMPROVE monitors are included here for summary purposes. Plots have been developed for the average monthly concentr ations and the performance statistics for all of the most significant light scattering component species (Sulfate, Nitrate, and Organic Carbon) for the 20% best days and 20% worst days. Plots for individual IMPROVE monitors associated with Florida Class I areas are included in Appendix F.2.

The soccer plots of monthly concentrations (Figures. 6.2

- 1 and 6.2

- 2) show that values for nitrate generally fall outside of criteria performance thresholds. Sulfates and organic carbon generally fall within goal thres holds, with a couple of months falling just outside the goal thresholds but well within the criteria thresholds. Figure 6.2

- 3 contains separate soccer plots for each season. The seasonal plots emphasize poorer nitrate performance in the summer (does not even appear on the plots provided because performance is off scale with other constituents), when observed nitrate is quite low and predicted nitrate is even lower. When concentration is factored into performance criteria, nitrate performance improves wit h respect to MFB and MFE (Figures 6.2

- 4

and 6.2

- 5)

Additionally, performance assed at the “one atmosphere” level was also deemed acceptable for ozone and particulate matter at various monitoring sites (STN, FRM, CASTNet, etc.). Overall,

VISTAS found th e Base G2 modeling results to be representative and acceptable for use in modeling projection for ozone, particulate matter, and regional haze.39 Figure 6.2

- 1: Soccer plot depicting both the mean fractional error and fractional bias for
component concent ration for all VISTAS sites based on Base G2 results. Each point represents a monthly value as compared to the model performance criteria (red box) and modeling performance goals (green box).

Figure 6.2

- 2: A zoomed view of the soccer plot depicting bo th the mean fractional error and fractional bias for component concentration for all VISTAS sites based on Base G2 results. Each point represents a monthly value as compared to the model performance criteria (red box) and modeling performance goals (green box). All Sites CMAQ 12km - 2002 Monthly

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0

-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0

Fractional BiasMean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal

All Sites CMAQ 12km - 2002 Monthly

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0

Fractional BiasMean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal40 Figure 6.2

- 3: Seasonal soccer plots based on Base G1 results for all VISTAS IMPROVE

monitors.

Figure 6.2

- 4: Bugle plot of the mean fraction bias for particulate matter and its component

concentrations for all VISTAS sites based on Base G2 re sults. Each point represents a monthly mean fraction bias value as compared to the model performance criteria (red lines) and modeling performance goals (green lines). All Sites CMAQ 12km - 2002 Monthly

0.0 50.0 100.0 150.0 200.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 Average Concentration ( **μ** g/m 3 )Mean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal41 Figure 6.2

- 5: Bugle plot of mean fraction error for particulate matter and its compon ent species for all VISTAS sites based on Base G2 results. Each point represents a monthly mean fraction bias value as compared to the model performance criteria (red lines) and modeling performance goals (green lines).

6.3 Florida Class 1 Areas Performance

The following section provides stack bar charts comparing observed fine particulate matter composition and modeled fine particulate matter composition. The charts have been split into two charts, with the first displaying the 20 % best days followed by the chart for the 20% worst days. Stacked bar charts have been developed for each of the four IMPROVE monitoring sites located in Florida: Chassahowitzka Wilderness (CHAS), Everglades National Park (EVER), and

St. Marks Wilderness (SAMA), as well as the adjacent Class I area in Georgia: Okefenokee (OKEF) (See Figure 6.3.1

- 6.3.4).

The stacked bar chart allows a side by side comparison of each days observed and modeled compositional and total light extinction. Within each bar the color codes are:

**•** Yellow = light extinction due to sulfates (bextSO4)

**•** Red = light extinction due to nitrates (bextNO3)

**•** Green = light extinction due to organic carbon (bextOC)

**•** Black = light extinction due to elemental (bextEC)

**•** Brown = light extinction due t o soil (bextSoil)

**•** Grey = light extinction due to coarse mass (bextCM)

The components are presented in the same order for both the observed (left hand bar) and modeled bar (right hand bar), so it easy to identify days when the prediction light extinction f or the component differs from the observed. The total height of the bar provides the total reconstructed particulate matter light extinction value. All Sites CMAQ 12km - 2002 Monthly

-200.0 -150.0 -100.0

-50.0

0.0 50.0 100.0 150.0 200.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 Average Concentration ( **μ** g/m 3 )Mean Fractional Bias Sulfate

Nitrate

Organics

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria42 A cursory view of the stacked bars charts reiterates that sulfates are a large contributor to light

extin ction in many of the Florida Class I areas and Okefenokee during most of the 20% best and 20% worst days. The bar charts also suggest organic carbon and nitrates are other significant components in light extinction on the 20% best days. Concerning the 20 % worst days, nitrates are not as significant and sulfates and organic carbon dominate the light extinction in both modeling and observations. Overall, modeling performance tends to under predict most species of PM on average at all four sites. Model per formance is better during the 20% best days, where under prediction primarily takes place in the nitrates. During the 20% worst days, sulfates are a large source of the light extinction under prediction. Modeling performance is best at St. Marks

and Chas sahowitzka, with performance in the Everglades proportionally worse for the 20% best days. Okefenokee has a large amount of under prediction, primarily concerning sulfates, during the 20% worst days. There are several days at each Florida Class I area wh ere a large over or under prediction occurs, particularly concerning sulfates (20% best days) and organic carbon (20% worst days).

Nitrates tend to be under predicted at all sites during the 20% best and worst days, especially during the summer. The model performance evaluation prepared by Environ recognizes the consistent issues with nitrate under prediction and attribute it to several causes, including errors with convective rainfall in the meteorological model and uncertainty in sea salt and ammoni a concentrations. The nitrate under prediction is exaggerated during extremely low levels of nitrates (< 1.0 μg/m 3 ) and at coastal sites, such as the Everglades National Park. In the stacked bar charts for the Everglades, nitrates are completely missing in the modeled contributions in both the 20% best and 20% worst days.

Overall, the FLDEP found model performance to fall within acceptable limits for model performance, although the performance for the Everglades falls within the lower levels of

accep tability. Due to the poor modeling of nitrates in the Everglades, modifications to the

relative reduction factor calculation were applied to correct the performance issues. This is

discussed further in section 7.2.4. The FLDEP further asserts the one at mosphere modeling performed by the VISTAS contractors is representative of conditions in the southeastern states and is applicable for use in attainment demonstrations.43 6.3.1 Chassahowitzka, FL

Best 20% Obs (left) vs 2002gt2a (right) at CHAS1

0 10 20 30 40 50 60 70 80 90

38 68 71 104 146 161 164 170 173 176 182 191 203 224 227 239 242 248 266 320 344 347

_ _ _ Avg

Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO444 Figure 6.3

- 1: Stacked bar chart for CHAS1 on the 20% best days (top) and 20% worst

days (bottom). Observed composition is presented in the left hand bar, with modeled composition from the 12km CMAQ represented by the right hand bar. 6.3.2 Everglades, FL Worst 20% Obs (left) vs 2002gt2a (right) at CHAS1

0 20 40 60 80 100 120 140 160 180 200

5 17 26 41 44 47 56 89 116 128 155 200 206 236 260 278 281 290 296 329 332 341 350

_ _ Avg

Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs 2002gt2a (right) at EVER1

0 5 10 15 20 25 30

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302

_ Avg

Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO445 Figure 6.3.2: Stacked bar chart for EVER1 on the 20% best days (top) and 20% worst days (bottom). Observed composition is presented in the left hand bar, with modeled composition from the 12km CMAQ represented by the right hand bar.

6.3.3 Okefenokee, GA Worst 20% Obs (left) vs 2002gt2a (right) at EVER1

0 20 40 60 80 100 120 140 160 180 200

5 35 44 56 62 74 95 98 113 125 128 152 158 218 236 257 281 290 305 311 332 341 350 362

_ Avg

Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Best 20% Obs (left) vs 2002gt2a (right) at OKEF1

0 10 20 30 40 50 60

11 20 38 59 62 104 170 173 176 179 191 239 242 257 266 275 284 335 344 347 356 359

_ _ _ Avg

Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO446 Figure 6.3.3: Stacked bar chart for OKEF1 on the 20% best days (top) and 20% worst days (bottom). Observed composition is presented in the left hand bar, with modeled composition from the 12km CMAQ represented by the right hand bar. Worst 20% Obs (left) vs 2002gt2a (right) at OKEF1

0 50 100 150 200 250 300 350

17 23 44 56 89 95 122 128 155 188 197 200 218 236 254 260 263 281 296 308 338 341 350

_ _ Avg

Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO447 6.3.4 Saint Marks, FL

Figure 6.3.4: Stacked bar chart for SAMA on the 20% best days (top) and 20% worst days (bottom). Observed composition is presented in the left hand bar, with modeled composition represented by the right hand bar. Best 20% Obs (left) vs 2002gt2a (right) at SAMA1

0 10 20 30 40 50 60 70 80 90 100

11 14 20 32 38 68 80 158 161 173 176 179 194 206 209 218 224 239 266 302 344 356

_ _ _ Avg

Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Worst 20% Obs (left) vs 2002gt2a (right) at SAMA1

0 50 100 150 200 250 300

35 44 47 56 89 95 98 107 116 122 125 128 131 155 185 188 197 200 260 290 293 296 341

_ _ Avg

Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO448 

### 7.0 LONG

- TERM STRATEGY FOR FLORIDA CLASS I AREAS

As stated in section 1.3 above, the regional haze rule requires States to establish reasonable progress goals, expressed in deciviews, for visibility improvement at each affected Class I area covering each (approximately) 10

- year period until 2064. This first set of reasonable progress goals must be met through measures contained in the state’s long

- term strategy covering the period from the baseline until 2018. This section discusses development of Florida ’s long

- term
strategy.

7.1 Overview of the Long

- Term Strategy Development Process

The monitored data and modeling analyses cited in sections 2 and 5 above establish that for the VISTAS region, the key contributors to regional haze in the 2000

- 2004 baseline timeframe were large stationary source s of sulfur dioxide emissions. Keeping that key conclusion in mind, this section addresses the following questions:

a. Assuming implementation of existing federal and state air regulatory requirements in

Florida and the VISTAS region, how much visibility i mprovement, compared to the glidepath, would we expect to see at Class I areas in or near Florida between now and

2018?

b. If additional emission reductions were needed, from what pollutants and source categories would the greatest visibility benefits be rea lized between the baseline and

2018?

c. How do we select any additional sources that would provide potentially significant visibility benefits in making reasonable progress?

d. What is the process for identifying additional emission controls or reductions that represent reasonable progress for those specific sources?

e. What additional emission controls represent BART in Florida ?

f. Given the additional emission reductions expected from BART and reasonable progress improvements , how much additional visibility impr ovement, compared to the glidepath, will we expect to see at Class I areas in Florida between the baseline

and 2018?49 7.2 Expected Visibility Results in 2018 for Florida Class I Areas under existing and

planned emissions controls ( G2 and, Best and Final Inventory (G4) )

There are significant control programs being implemented between the baseline period and 2018. These programs are described in more detail below.

7.2.1 Federal and State Control Requirements

CAIR

. CAIR will permanently cap emissions of SO 2 and NOx from EGUs in the eastern United States by 2015. When fully implemented, CAIR will reduce SO 2 emissions from EGUs in these states by more than 70 percent, and NOx emissions by more than 60 percent, from 2003 levels.

The CAIR rule was remanded in December 2008, to correct for deficiencies, but remains in effect until a replacement rule is finalized. The proposed replacement (Transport Rule) was published on August 2, 2010 (75 FR 45210) and is expected to be final by mid 2011.

NOx SIP Call . Phase I of the NO x SIP call applies to certain EGUs and large non

- EGUs, including large industrial boilers and turbines, and cement kilns. Those states affected by the NOx SIP call in the VISTAS region have developed rules for the control of NOx emissi ons that have been approved by the USEPA. The NOx SIP Call has resulted in a 68 percent reduction in NOx emissions from large stationary combustion sources. For this analysis, we capped the

emissions for NOx SIP call

- affected sources at 2007 levels, and carried forward the capped levels for the 2009 and 2018 future year inventories.

North Carolina CSA

. Under the act, enacted in 2002, coal

- fired power plants in North Carolina

must achieve a 77

- percent cut in NOx emissions by 2009 and a 73

- percent cut i n SO 2 emissions by 2013. This legislation establishes annual caps on both SO 2 and NOx emissions for the two primary utility companies in North Carolina, Duke Energy and Progress Energy. These reductions must be made in North Carolina, and allowances are not saleable.

**•** Under a settlement agreement, by 2008, Tampa Electric will install permanent

emissions

- control equipment to meet stringent pollution limits; implement a series of

i nterim pollution

- reduction measures to reduce emissions while the permanent controls are designed and installed; and retire pollution emission allowances that Tampa Electric or others could use, or sell to others, to emit additional NOx, SO 2 , and PM. Consent Agreements (TECO, VEPCO ) and Gulf Power Crist 7 Voluntary Agreement

.

**•** Virgi nia Electric and Power Co. (VEPCO) agreed to spend $1.2 billion by 2013 to
eliminate 237,000 tons of SO 2 and NOx emissions each year from eight coal

- fired electricity generating plants in Virginia and West Virginia.

**•** A 2002 agreement calls for Gulf Power t o upgrade its operation to cut NOx emission rates by 61 percent at its Crist generating plant by 2007, with major reductions beginning in early 2005. The Crist plant is a significant source of NOx emissions in the Pensacola

area.

One

- hour ozone SIPs (Atlanta / Birmingham / Northern Kentucky). New SIPs have been

submitted to the USEPA to demonstrate attainment of the one

- hour ozone National Ambient Air50 Quality Standard (NAAQS). These SIPs require NOx reductions from specific coal fired power plants a nd address transportation plans in these cities.

NOx RACT in 8

- hour Nonattainment Area SIPs . The NCDAQ’s SIP for the Charlotte / Rock

Hill / Gastonia nonattainment area includes Reasonable Achievable Control Technology (RACT) for NOx for two facilities located in the nonattainment area: Philip Morris USA and Norandal USA. These controls were also modeled in 2018. Additional RACT controls may be real ized as other companies subject to RACT complete the determination, but RACT

- level controls were assumed for just these two sources.

Heavy Duty Diesel (2007) Engine Standard (for onroad trucks and buses). USEPA set a PM emissions standard for new heavy

- d uty engines of 0.01 grams per brake

- horsepower

- hour (g/bhp

- hr), to take full effect for diesel engines in the 2007 model year. This rule also includes

standards for NOx and non

- methane hydrocarbons (NMHC) of 0.20 g/bhp

- hr and 0.14 g/ bhp

- hr, respectively. These NOx and NMHC standards will be phased in together between 2007 and 2010, for diesel engines. Sulfur in diesel fuel must be lowered to enable modern pollution

control technology to be effective on these trucks and buses. USEPA will require a 97 perc ent reduction in the sulfur content of highway diesel fuel from its current level of 500 parts per million (low sulfur diesel, or LSD) to 15 parts per million (ultra

- low sulfur diesel, or ULSD).

Tier 2 Tailpipe (Onroad vehicles) . USEPA mobile rules inclu de the Tier 2 fleet averaging program, modeled after the California LEV II standards. Manufacturers can produce vehicles with emissions ranging from relatively dirty to zero, but the mix of vehicles a manufacturer sells each year must have average NO

x emis sions below a specified value

. Tier 2 standards became effective in the 2005 model year.

Large Spark Ignition and Recreational Vehicle Rule . USEPA has adopted new standards for emissions of oxides of nitrogen (NOx), hydrocarbons (HC), and carbon mono xide (CO) from several groups of previously unregulated nonroad engines. Included in these are large industrial spark

- ignition engines and recreational vehicles. Nonroad spark

- ignition engines are those powered by gasoline, liquid propane gas, or compres sed natural gas rated over 19 kilowatts (kW) (25 horsepower). These engines are used in commercial and industrial applications, including forklifts, electric generators, airport baggage transport vehicles, and a variety of farm and construction application s. Nonroad recreational vehicles include snowmobiles, off

- highway motorcycles, and all

- terrain

- vehicles. These rules were initially effective in 2004 and will be fully phased

- in by 2012.

Nonroad Diesel Rule . This rule sets standards that will reduce emi ssions by more than 90 percent from nonroad diesel equipment, and reduce sulfur levels by 99 percent from current levels in nonroad diesel fuel starting in 2007. This step will apply to most nonroad diesel fuel in

2010 and to fuel used in locomotives and m arine vessels in 2012. (http://www.epa.gov/nonroad

diesel/)

Industrial Boiler/Process Heater/RICE MACTs . The USEPA originally issued final rules to substantially reduce emissions of toxic air pollutants from industrial, commercial and

institutional boile rs, process heaters and from stationary reciprocating internal combustion51 engines (RICE). These rules reduce d emissions of a number of toxic air pollutants, including hydrogen chloride, manganese, lead, arsenic and mercury by 2009. These rules also reduced

emissions of SO 2 and PM in conjunction with the toxic air pollutant reductions. The applied Maximum Achievable Control Technology (MACT) control efficiencies were 4 percent for SO 2 and 40 percent for PM10 and PM2.5. While the USEPA’s industrial boiler MA CT rules were vacated on June 8, 2007 , t he VISTAS “best and final” modeling run includes these reductions. It

was the consensus of the VISTAS states that these reductions would eventually be reinstated in some form well prior to 2018.

On June 4 , 2010, th e USEPA published the proposed Industrial, Commercial, and Institutional

B oiler and Process Heater Rule (Boiler MACT , (75 FR 32006) ). The proposed Boiler MACT affects numerous relatively large boilers in Florida, including one coal

- fired unit (Smurfit

- Ston e in Fernandina Beach) that has been identified as a unit to be addressed within the reasonable progress provisions for SO 2 reductions. The acid gas emissions requirement of this proposed rule would have the significant co

- benefit of further reducing SO 2 emissions for many of the subject sources.

Combustion Turbine MACT . The projection inventories do not include the NO x co

- benefit effects of the MACT regulations for Gas Turbines or stationary Reciprocating Internal Combustion Engines, which USEPA es timates to be small compared to the overall inventory.

VOC 2

- , 4

- , 7

- , and 10

- year MACT Standards . Various point source MACTs and associated emission reductions were implemented. We did not count reductions occurring before 2002.

7.2.2 Additional State programs to reduce emissions

In addition to accounting for specific emission reductions due to ongoing air pollution programs as required under the RHR section 308 (d)(3)(v)(A), states are also required to consider the air quality benefits of measures to mitigate the impacts of construction activities [section 308(d)(3)(v)(B)] and agricultural and forestry smoke management [section 308(d)(3)(v) (E)].

With regard to the impact of construction activities, rule 62

- 296.320, F.A.C., General Pollution

Emissi on Limiting Standards addresses construction related activities. In particular, section (4)(c) Unconfined Emissions of Particulate Matter provides that reasonable precautions be taken to prevent or eliminate emissions. For example, the rule addresses pav ing and maintenance of roads, parking areas, and yards and the application of water or chemicals to control emissions during construction.

With regard to smoke management, Florida has a certified Smoke Management Plan (SMP) meeting the intent of EPA’s Interim Air Quality Policy on Wildland and Prescribed Fires. The Environmental Protection Agency, Region 4

, acknowledged receipt of this SMP and its certification in February 2002.

The SMP follows the requirements for such a plan contained in the Interim Air Quality Policy on Wildland and Prescribed Fires. The Florida Division of Forestry operates a burn authorization program that considers the potential for smoke from the burn impacting smoke sensitive52 receptors (e.g., airpo rts, roads, hospitals, urban areas, etc.). The SMP provides alternative s for burning and is considerate of minimizing air pollutants. The SMP is located in Appendix N.

The Prevention of Significant Deterioration (PSD) new source review program also affe cts visibility in Class I area by assuring that major new or modified sources of visibility

- affecting pollutants are installed with the best available control technology. The air quality review required for PSD sources includes an analysis of visibility i mpacts. The results of this analysis weighs

- in on the determination of the allowed emissions and required controls for the sources

.

7.2.3 Projected 2009 and 2018 G2 and Best and Final (G4) Emissions Inventories

The G2 and Best and Final ( G4 ) inventor y fo r 2018 account for post

- 2002 emission reductions from promulgated and proposed federal, state, local, and site

- specific control programs as of July 1, 2004. In general, the G2 emissions inventories were developed for 2009 and 2018 using

current control information in Florida , and growing the 2002 inventory using EGAS6, MOBILE6, and IPM for electric generating units (EGUs). In addition, the Best and Final (G4) inventory for 2018 includes any known BART and Reasonable Progress emissions changes , and changes to specific oil

- fired EGUs in Florida to adjust for the IPM solution which assumed complete conversion to natural gas

. I t should be noted tha t very few of the BART and

Reasona ble Progress changes were available at the time of the last modeling run. For Florida, the only the Georgia Pacific Palatka BART changes were included. Many of the projections contained in this document are based on the G2 inventory, however, when availa ble, some figures and results are based on the Best and Final inventory. The difference between the two

inventories is limited to a relatively few stationary point source changes that were updated for the purpose of the final modeling run. These changes mostly affect sulfur dioxide emissions (52,540 tons addition) with the Best and Final emissions having greater values, however on a

state

- wide basis, either inventory provides similar qualitative results.

For EGUs, IPM results were adjusted based on State and local (S/L) agency knowledge of planned emission controls at specific EGUs (including the changes to the oil

- fired units in Florida mentioned in the previous paragraph) . These updates for G4 are documented in the MACTEC emissions inventory report “Do cumentation of the 2002 Base Year and 2009 and 2018 Projection Year Emission Inventories for VISTAS” dated July 2007. (Appendix D)

For non

- EGUs, we used recently updated growth and control data consistent with the data used in USEPA’s CAIR analyses supplem ented by State and local agency data and updated forecasts from the Department of Energy (DOE).

Area source controls were estimated using known state level Stage I controls on gasoline dispensing facilities and open burning estimates, as well as controls used to project emissions for USEPA’s Heavy Duty Diesel rulemaking and for the CAIR rulemaking.

Mobile source controls included local controls underlying the 2002 baseline inventory (vehicle emission inspection, Stage II vapor recovery, anti

- tampering, et c.) with changes based on specific State input. Nonroad control data and projections for 1996, 2010, 2015, and 2020 were53 obtained from the USEPA's Clean Air Interstate Rule (CAIR) Technical Support Document, and straight line projections were used to esti mate 2009 and 2018 levels.

**•** The following bar charts show expected decreases in emissions of SO 2 and NOx across the VISTAS states from 2002 through 2018. Note that for SO 2 emissions in particular, which are the largest contributors to haze, emissions from electric generating facilities are expected to decrease dramatically ( 57 percent in Florida ) between 2002 and 2018. However, even after implementation of CAIR, EGU emissions are projected to remain the largest contributor to haze, comprising more than ha lf of remaining SO 2 emissions in

every state.

Figure 7.2.3

- 1. Base G2 Annual SO 2 emissions for 2002, 2009, and 2018 in the VISTAS

states. West Virginia Alabama Florida Kentucky Georgia Mississippi South Carolina North Carolina Tennessee Virginia 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU 2002

2009

2018

West Virginia Alabama Florida Kentucky Georgia Mississippi South Carolina North Carolina Tennessee Virginia 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU 2002

2009

2018

Alabama Florida Kentucky Georgia Mississippi South Carolina North Carolina Tennessee Virginia 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU 2002

2009

201854 Figure 7.2.3

- 2. Base G2 Annual NOx emissions in 2002, 2009, and 2018 in the VISTAS

States.

Summary of Final Base Emissions Inventories for 2009 and 2018

Table 7.2.3

- 1 is a summary of the 2009 Base G2 emission inventory. The complete inventory and discussion of the methodology is contained in Appendix D.

Table 7.2.3

- 1. 2009 Emissions Inventory Summary for FL

. CO NH 3 NOx PM10 PM2.5 SO 2 VOC

EGU 35,928 1,631 86,165 9,007 5,910 186,055 1,910

NONEGU 87,037 1,544 46,020 39,872 33,946 65,511 36,880

ONROAD 3,308,863 21,549 312,321 9,801 6,104 2,584 336,707

NONROAD 2,104,920 148 163,794 15,613 14,866 8,967 209,543

AREA 57,011 38,616 28,187 503,230 64,589 36,699 420,172

FIRES 923,310 3,157 19,791 98,470 88,756 4,129 51,527

BIOGENICS 0 0 36,320 0 0 0 1,522,031

TOTAL 6,517,070 66,645 692,599 675,993 214,170 303,943 2,578,768

Table 7.2.3

- 2 is a summary of the 2018 G4 emission inventory. The complete inventory and discussion of the methodology is contained in Appendix D. 0 100 200 300 400 500 600 700 800 900 1,000

Alabama Florida Georgia Kentucky Mississippi North Carolina South Carolina Tennessee Virginia West VirginiaAnnualNOx(Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2002

2009

2018

0 100 200 300 400 500 600 700 800 900 1,000

Alabama Florida Georgia Kentucky Mississippi North Carolina South Carolina Tennessee Virginia West VirginiaAnnualNOx(Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2002

2009

201855 Table 7.2.3

- 2. 2018 Emissions Inventory Summary for FL

. CO NH 3 NOx PM10 PM2.5 SO 2 VOC

EGU 54,146 2,976 74,640 10,022 6,886 138,340 2,422

NONEGU 96,819 1,829 51,902 46,456 39,430 75,047 42,811

ONROAD 2,520,315 25,885 148,486 8,178 3,994 2,506 219,554

NONROAD 2,323,327 171 127,885 12,497 11,868 7,536 183,452

AREA 53,903 40,432 30,708 578,516 72,454 38,317 489,975

FIRES 923,310 3,157 19,791 98,470 88,756 4,129 51,527

BIOGENICS 0 0 36,320 0 0 0 1,522,031

TOTAL 5,971,821 74,450 489,733 754,139 223,388 265,875 2,511,772

7.2.4 Model Results for the 2018 Base Best and Final (G4) Inventory Compared to the

Uniform Rate of Progress Glidepaths for FL Class I Areas

Using 2000

- 2004 IMPROVE monitoring data, the deciview values for the 20 percent best days in each year are averaged together, producing a single average deciview value f or the best days. Similarly, the deciview values for the 20 percent worst days in each year are averaged together, producing a single average deciview value for the worst days.

Figures 7.2.4

- 1 through 7.2.4

- 10 illustrate the predicted visibility improv ement by 2018 resulting

from the Best and Final inventory that represents implementation of existing federal and state regulations, and limited BART and reasonable progress compared to the Uniform Rate of Progress glidepath for Everglades National Park ( EV ER ), Chassahowitzka National Wilderness Area (CHAS), St. Marks National Wilderness Area (SAMA), Okefenokee National Wilderness Area (OKEF), and Breton Island National Wilderness Area (BRET) . The green lines show the target natural condition in 2064 for th e 20 percent worst days or 20 percent best days . The red

line shows incremental deciview changes resulting from a uniform rate of progress (uniform glide slope) between current and natural conditions in 2064, for average 20 percent worst visibility days o r 20 percent best visibility days in the 2000

- 2004 baseline period. The purple lines show the improvement expected from existing and planned emission controls during the period of the first long term strategy. The legend for the purple line refers to Met hod 1

— this is the EPA method of averaging all 20% worst days in 2002 without consideration of model performance.

The “a” version of f igures 7.2.4

- 1 to 7.2.4

- 5 represent the projected glidepaths based on the CMAQ 12 km results using the new IMPROVE equation without any corrections for model performance

. The “b” version of figures 7.2.4

- 1 to 7.2.4

- 5 make corrections to the calculation of

the relative reduction f actors to account for poor model performance in replicating NO3 and SO4

concentrations. It was found that NO3 levels were greatly under

- predicted by the model compared to the actual measurements in some areas, with the Everglades being affected the

most. Also, but to a lesser extent, there were some days in which SO4 model performance was

poor . The consequence of this poor model performance is that the relative reduction factors56 developed from the model predictions would be adversely affected if these days are included in

the calculation

. It was our best judgment that the reliability of the relative reduction factor would be improved if the calculation took model performance into account. Thus, s ome limited corrections were made in developing the rela tive reduction factors to account for those days in which model performance did not meet certain expectations. The details of this correction are developed in the Technical Support Document, section 5.3.3 (Appendix F1 ).

Figure 7.2.4

- 1 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at Everglades National Park.57 Figure 7.2.4

- 1b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Everglades National Park. (Corrected for NO3 and SO4 model performance )58 Figure 7.2.4

- 2 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at Chassahowitzka Wilderness Area.59 Figure 7.2.4

- 2b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at ChassahowitzkaWilderness Area. (Corrected for NO3 and SO4 model performance)60 Figure 7.2.4

- 3 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at St. Marks Wilderness Area.61 Figure 7.2.4

- 3b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progr ess at St. Marks Wilderness Area. (Corrected for NO3 and SO4 model performance)62 Figure 7.2.4

- 4 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at Okefenokee Wilderness Area.63 Figure 7.2.4

- 4b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Okefenokee Wilderness Area. (Corrected for NO3 and SO4 model performance)64 Figure 7.2.4

- 5 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at Breton Island Wildlife Refuge.65 Figure 7.2.4

- 5b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Breton Island Wildlife Refuge. (Corrected for NO3 and SO4 model performance)66 Figure 7.2.4

- 6 CMAQ 2018 Best and Final results at Everglades National Park. 

### Reasonable Progress Assessment Base G2 inventory, new IMPROVE Algorithm Everglades - Best 20% Days

11.69 11.69 11.69

11.35 11.46

5.21

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction67 Figure 7.2.4

- 7 CMAQ 2018 Best and Final results at Chassahowitzka Wilderness Area. 

### Reasonable Progress Assessment Base G2 inventory, new IMPROVE Algorithm Chassahowitzka - Best 20% Days

15.51 15.51 15.51

13.83 13.91

5.91

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction68 Figure 7.2.4

- 8 CMAQ 2018 Best and Final results at St. Marks Wilderness Area. 

### Reasonable Progress Assessment Base G2 inventory, new IMPROVE Algorithm Saint Marks - Best 20% Days

14.37 14.37 14.37

13.15 12.80

5.39

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction69 Figure 7.2.4

- 9 CMAQ 2018 Best and Final results at Okefenokee Wilderness Area. 

### Reasonable Progress Assessment Base G2 inventory, new IMPROVE Algorithm Okefenokee - Best 20% Days

15.23 15.23 15.23

14.25 13.92

5.31

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction70 Figure 7.2.4

- 10 CMAQ 2018 Best and Final results at Breton Island Wildlife Refuge.

Tables 7.2.4

- 1 and 7.2.4

- 2 are provided to show the pollutant

- specific extinction without and with the model performance correction, respectively. The data shows the unusu ally large percentage increase in NO3 extinction when the poor performance data is included. 

### Reasonable Progress Assessment Base G2 inventory, new IMPROVE Algorithm

Breton - Best 20% Days

14.04 14.04 14.04

13.75 13.50

5.33

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction71 Table 7.2.4

- 1

Class I Area Name State IMPROVE

Site #
Days

in

2002 2000

- 2004

Baseline

dV Natural Background dV 2018 dV

Reduction
Target Modeled

2018 dV

Reduction Difference

from dV

Reduction
Target

Chassahowitzka FL CHAS1 23 25.7 11.3 3.4 3.5

- 0.1

Everglades FL EVER1 24 22.3 12.3 2.3 1.5 0.8

Saint Marks FL SAMA1 23 26.3 11.6 3.4 3.3 0.1

Okefenokee GA OKEF1 23 27.1 11.5 3.6 3.3 0.3

Breton LA BRET1 24 26.0 12.3 3.2 2.8 0.4

Percent Change in Average Extinction (Mm

- 1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS

Chassahowitzka FL CHAS1

- 30%

- 43%

- 2%

- 6%

- 29% 22% 28% 0%

Everglades FL EVER1

- 12%

- 39% 131%

- 6%

- 22% 2% 7% 0%

Saint Marks FL SAMA1

- 28%

- 42% 17%

- 4%

- 12% 12% 6% 0%

Okefenokee GA OKEF1

- 28%

- 44% 37%

- 6%

- 14% 16% 8% 0%

Breton LA BRET1

- 24%

- 41% 68% 4%

- 9% 14% 0% 0%

Absolute Change in Average Extinction (Mm

- 1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS

Chassahowitzka FL CHAS1

- 40.3

- 37.9

- 0.1

- 1.3

- 1.9 0.2 0.8 0.0

Everglades FL EVER1

- 14.0

- 16.5 6.3

- 2.8

- 1.3 0.0 0.2 0.0

Saint Marks FL SAMA1

- 39.9

- 39.3 0.8

- 0.9

- 0.7 0.1 0.1 0.0

Okefenokee GA OKEF1

- 45.4

- 45.0 1.8

- 1.8

- 0.9 0.1 0.2 0.0

Breton LA BRET1

- 34.0

- 39.9 5.8 0.4

- 0.5 0.1 0.0 0.0

2000

- 2004 Baseline Average Extinction (Mm

- 1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS

Chassahowitzka FL CHAS1 135.0 87.4 5.1 20.6 6.7 0.9 3.0 0.2

Everglades FL EVER1 116.4 42.5 4.8 47.1 5.9 0.9 3.0 1.2

Saint Marks FL SAMA1 142.1 93.1 4.5 24.2 5.6 0.8 2.6 0.2

Okefenokee GA OKEF1 159.4 102.9 4.9 30.3 6.2 0.8 3.1 0.2

Breton LA BRET1 139.0 97.0 8.5 11.7 5.2 1.0 3.8 0.772 Table 7.2.4

- 2

Class I Area Name State IMPROVE

Site #
Days

in

2002 2000

- 2004

Baseline

dV Natural Background dV 2018 dV

Reduction
Target Modeled

2018 dV

Reduction Difference

from dV

Reduction
Target

Chassahowitzka FL CHAS1 23 25.7 11.3 3.4 3.5

- 0.1

Everglades FL EVER1 24 22.3 12.3 2.3 2.5

- 0.2

Saint Marks FL SAMA1 23 26.3 11.6 3.4 3.4 0.0

Okefenokee GA OKEF1 23 27.1 11.5 3.6 3.4 0.3

Breton LA BRET1 24 26.0 12.3 3.2 3.8

- 0.6

Percent Change in Average Extinction (Mm

- 1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS

Chassahowitzka FL CHAS1

- 30%

- 43%

- 2%

- 6%

- 29% 22% 28% 0%

Everglades FL EVER1

- 18%

- 40% 0%

- 6%

- 23% 2% 7% 0%

Saint Marks FL SAMA1

- 29%

- 43% 17%

- 4%

- 12% 12% 7% 0%

Okefenokee GA OKEF1

- 29%

- 44% 37%

- 6%

- 14% 16% 9% 0%

Breton LA BRET1

- 32%

- 46% 0% 4%

- 11% 16% 0% 0%

Absolute Change in Average Extinction (Mm

- 1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS

Chassahowitzka FL CHAS1

- 40.3

- 37.9

- 0.1

- 1.3

- 1.9 0.2 0.8 0.0

Everglades FL EVER1

- 21.1

- 17.1 0.0

- 2.9

- 1.3 0.0 0.2 0.0

Saint Marks FL SAMA1

- 40.8

- 40.2 0.8

- 0.9

- 0.7 0.1 0.2 0.0

Okefenokee GA OKEF1

- 46.1

- 45.7 1.8

- 1.8

- 0.9 0.1 0.3 0.0

Breton LA BRET1

- 44.3

- 44.3 0.0 0.5

- 0.6 0.2 0.0 0.0

Absolute Change in Average Extinction (Mm

- 1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS

Chassahowitzka FL CHAS1

- 40.3

- 37.9

- 0.1

- 1.3

- 1.9 0.2 0.8 0.0

Everglades FL EVER1

- 21.1

- 17.1 0.0

- 2.9

- 1.3 0.0 0.2 0.0

Saint Marks FL SAMA1

- 40.8

- 40.2 0.8

- 0.9

- 0.7 0.1 0.2 0.0

Okefenokee GA OKEF1

- 46.1

- 45.7 1.8

- 1.8

- 0.9 0.1 0.3 0.0

Breton LA BRET1

- 44.3

- 44.3 0.0 0.5

- 0.6 0.2 0.0 0.0

In Class I areas in Florida , visibility improvements on the 20 percent worst days are expected to

be slightly better than the uniform rate of progress glidepath by 2018 for Everglades and Chassahowitzka, and slightly less than the uniform rate of progress for St. Marks based on reductions from existing and planned emissions controls. Visibility improvement in nearby73 C lass I areas outside of Florida (Okefenokee and B reton) , for which Florida sources may impact, are all slightly worse and slightly better, respectively, than the uniform rate of progress. In all cases, the rate of visibility improvement is hovering near the uniform rate of progress.

Additional emission s reductions from application of BART or reasonable progress requirements, which have not been taken in to account in the final modeling run, would provide additional visibility improvement.

In addition to improving visibility on the 20 percent worst visibility days, states are also required

to assure that visibility on the 20 percent best days at the Class I areas does not degrade

. Figures

7.2.4

- 6 to 7.2.4

- 10 show that visibility on the best days improves.

7.3 Relative Contribution from Internat ional Emissions to Visibility Impairment in 2018 at

VISTAS Class I areas

Emissions from Mexico, Canada, Central America, Asia, and Africa contribute to PM2.5 loadings and visibility impairment at Class I areas in the continental United States. To evaluate the relative contribution of international emissions to visibility at Class I areas in the southeastern United States, VISTAS used a combination of modeling results from the global

three

- dimensional chemical transport model (GEOS

- Chem) and CMAQ. VISTAS u sed the

GEOS

- Chem global model to generate initial and boundary conditions for the CMAQ modeling

domain. GEOS

- Chem was run for the 2002 modeling year using a 4 x 5 degree horizontal grid

resolution and a 3

- hour temporal resolution. Because emissions were based on monthly averages, the model does not capture the episodic variability in emissions. The GEOS

- Chem outputs were used to calculate initial and boundary conditions for the national CMAQ modeling domain. The national CMAQ domain included portions of Canada and Mexico, so emissions for these countries were included within the national CMAQ modeling domain or as part of the boundary conditions outside the national modeling domain, as appropriate.

Two complementary methods were used to calculate the impact of international emissions at Class I areas. Because all international fires are treated as natural emissions in these runs, both cases underestimate the contributions from international fires that are due to anthropogenic burning.

1) Internationa l emissions are represented by the difference between two GEOS

- Chem runs. In the first run United States anthropogenic emissions were removed, and in the second run both United States and international anthropogenic emissions were removed. The

differenc e represents international anthropogenic emissions, in the absence of United States anthropogenic emissions (e.g. compared to 2064 levels). Harvard University provided GEOS

- Chem results to VISTAS for 2002 international contribution on 4 x 5 degree grid. Concurrently Harvard modeling for the Electric Power Research Institute (EPRI) provided GEOS

- Chem results for 2001 international contribution on a 1 x 1 degree grid scale.74 2) International emissions are represented by the difference between two CMAQ 36

- km simulations, both using 2018 Base F emissions and boundary conditions from GEOS

Chem. In the first CMAQ run, all global natural and anthropogenic emissions in 2018 are active. In the second CMAQ run, only global (United States and international)

natural emissions are active. Here the impacts of international emissions are compared against 2018 conditions rather than natural background conditions.

Separate from VISTAS, the CENRAP RPO used PM Source Apportionment Technology (PSAT) in the CAMx regional ai r quality model to calculate the impact of Canadian and Mexican emissions and boundary conditions on visibility at Class I areas in 2002.

As illustrated in Figure 7.3

- 1 for annual average contributions to sulfate at VISTAS and neighboring Class I areas, the estimated international contributions are higher at Class I areas near the Canadian and Mexican borders and along the eastern coast. Th e estimated international contribution is higher using CMAQ and PSAT than in the GEOS

- Chem runs because the grid scale is finer (more accurate dispersion of emissions) and because the background atmosphere includes loadings from current United States anthr opogenic emissions (greater photochemical activity). Similar charts for nitrate and organic carbon mass, for impacts on 20 percent worst visibility days, and for impacts of international emissions on calculated light extinction are included in Appendix H.

Figure 7.3

- 1. Estimated international emissions contributions to sulfate at VISTAS and neighboring Class I areas. 

### Transboundary Man-Made Impacts

0.0 0.2 0.4 0.6 0.8 1.0 1.2BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc. (**μ**g/m3)

GEOS_4x5 GEOS_1x1 PSAT CMAQ75 7.4 Relative Contributions to Visibility Impairment: Pollutants, Source Categories, and

Geographic Areas

An important step toward identifying further reasonable progress measures is to identify the key pollutants contributing to visibility impairment at each Class I area. To understand the relative benefit of further reducing emissions from different pollutants, source sectors, and geographic areas, VISTAS engaged the Georgia Institute of Technology to perform emission sensitivity model runs using CMAQ. Emissions sensitivities were initially performed for three episodes representing winter and summer conditions: Jan 2002, July 2001 , and July 2002. These runs used the initial 2018 projections inventory and considered 30 percent reductions from specific pollutants, source categories, and geographic areas. Emissions sensitivities were repeated using the 2009 Base D projection invento ry and two month

- long episodes from 2002: Jun 1

- Jul 10 and

Nov 19

– Dec 19. Emissions in 2009 were reduced by 30 percent for each pollutant sensitivity run. The pollutant contributions that were evaluated were:

**•** SO 2 from EGU sources in each VISTAS state , other RPOs in the VISTAS 12 km grid, and Boundary Conditions from outside the 12 km domain.

**•** SO 2 from non

- EGU point sources in each VISTAS state, other RPOs, and Boundary

Conditions

**•** NOx from ground level (on

- road plus non

- road plus area) sources in each V ISTAS state

and other RPOs

**•** NOx from point (EGU plus non

- EGU) sources in each VISTAS state and other RPOs

**•** NH3 from all sources in VISTAS and other RPOs

**•** Volatile Organic Compounds from anthropogenic and biogenic sources in the 12 km modeling domain

**•** Primary C arbon from all ground level sources in each VISTAS state and other RPOs

**•** Primary Carbon from all point sources in each VISTAS state and other RPOs

**•** Primary Carbon from all fires in each VISTAS state and other RPOs

Results are shown in Figures 7.4

- 1 through 7.4

- 4 below for the average of the 20 percent worst visibility days for each of three Florida Class I areas , plus Okefenokee . Responses for 20 percent worst days were calculated by averaging the responses of the 20 percent worst days that were

modeled in the two episodes. For the Florida sites, responses on four to five of the 20 percent worst visibility days were included in these graphics.76 Figure 7.4

- 1. CMAQ projections of visibility responses on 20 percent worst days at Everglades to 30 percent reductions from the 2009 Base D inventory for visibility

- reducing pollutants in different source categories and geographic areas.

Figure 7.4

- 2. CMAQ projections of visibility responses on 20 percent worst days at Chassahowitzka to 30 percent reductions from the 2009 Base D inventory for visibility

- reducing pollutants in different source categories and geographic areas. 

## Everglades, FL (Worst)

-4.50

-4.00

-3.50

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

SO2_EGU SO2_nonEGU NOx_Ground NOx_Point NH3 VOCs PC_Ground PC_Point PC_Fires**∆**Bext(Mm-1) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL



### Chassahowitzka, FL (Worst)

-8.00

-7.00

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

SO2_EGU SO2_nonEGU NOx_Ground NOx_Point NH3 VOCs PC_Ground PC_Point PC_Fires**∆**Bext(Mm-1) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL77 Figure 7.4

- 3. CMAQ projections of visibility responses on 20 percent worst days at St.

Marks to 30 percent reductions from the 2009 Base D inventory for visibility

- reducing pollutants in different source categories and geographic areas.

Figure 7.4

- 4. CMAQ projections of visibility responses on 20 percent worst days at Okefenokee to 30 percent reductions from the 2009 Base D inventory for visibility

- reducing pollutants in different source categories and geographic areas. 

## St._Marks, FL (Worst)

-16.00

-14.00

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

SO2_EGU SO2_nonEGU NOx_Ground NOx_Point NH3 VOCs PC_Ground PC_Point PC_Fires**∆**Bext(Mm-1) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL



## Okefenokee, GA (Worst)

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

SO2_EGU SO2_nonEGU NOx_Ground NOx_Point NH3 VOCs PC_Ground PC_Point PC_Fires**∆**Bext(Mm-1) Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL78 As Figures 7.4

- 1 through 7.4

- 4 illustrate, the greatest visibility benefits on the 20 percent worst days for the Florida Class I areas and Okefenokee are projected to result from further reducing

SO 2 from EGUs. The Everglades is somewhat different than any of the ot her Class I areas with a greater relative influence from carbon (VOC) and boundary conditions. Contributions from other RPOs are comparatively small and the greatest benefits would likely be from further EGU

reductions within Florida, Georgia, and Alab ama . Additional benefits are projected from SO 2 emission reductions from non

- utility, industrial point sources. The pattern of relative SO 2 contributions from non

- EGUs among the various VISTAS states is similar to the pattern of

relative SO 2 contribution s from EGUs.

Because ammonium nitrate is a relatively small contributor to PM2.5 mass and visibility impairment on the 20 percent worst days at all the Class I areas, the benefits of reducing NOx

and NH3 emissions at these sites are small. For the 20 pe rcent best days ammonium nitrate has a somewhat larger contribution to visibility impairment. R educing ammonia emissions would be more beneficial for reducing ammonium nitrate contributions to visibility impairment than further reducing nitrogen oxide emi ssions from either ground or point sources.

VOCs do contribute to visibility impairment, but as shown in the charts above, this contribution

is largely from biogenic sources such as vegetative emissions. Controlling anthropogenic

sources of VOC emissio ns has little if any visibility benefit at the Class I areas. Reducing primary carbon from point sources, ground level sources or fires have a relatively small visibility

benefit on average, although fires can have a large impact on individual days . This is consistent with the monitoring data that shows that most of measured organic carbon is secondary in origin and primary carbon is only a small fraction of the total measured carbon (Appendix B).

Note that these results from the emission sensitivity ru ns are consistent with the conclusions

drawn from the 2000

- 2004 baseline monitoring data (see section 2.4 above). The results indicate that sulfate is the dominant contributor to visibility impairment on the 20 percent worst days at all sites, and that am monium nitrate may be important for sites where the 20 percent worst days occur in the winter. We conclude that reducing SO 2 emissions from EGU and non

- EGU point sources in the VISTAS states would have the greatest visibility benefits for the Florida and nearby Class I areas.

7.5 Relative Contributions to Visibility Impairment: Geographic Areas of Influence for

Florida Class I Areas

Once it was determined that SO 2 emission reductions from EGU and non

- EGU point sources in

the VISTAS states would be the most effective sources to control to improve visibility at the Florida and nearby Class I areas, the next step was to identify the specific geographic areas that most likely influence visibility in each Class I area, and then to identify the major SO 2 point sources located in those geographic areas. An SO 2 Area of Influence (AoI) was defined for each Class I area to represent the geographic area containing sources that would likely have the greatest impact on visibility at that Class I area. All SO 2 point sources within these Areas of79 Influence were identified and ranked by their 20 02 emissions. The following sections contain a broad overview of the steps in the Area of Influence analyses. See Appendix F1 for a more

detailed discussion of these anal yses and plots for additional Class I areas.

7.5.1 Back Trajectory Analyses

The first step was to generate meteorological back trajectories for IMPROVE monitoring sites in

Florida and neighboring Class I areas for the 2000

- 2004 baseline period. Back traj ectory analyses use interpolated measured or modeled meteorological fields to estimate the most likely central path of air masses that arrive at a receptor at a given time. The method essentially follows a parcel of air backward in hourly steps for a spec ified length of time. Figure 7.5.1 is an example of a back trajectory analysis for Everglades National Park for the 20 percent worst days

in 2002.

Trajectories were started at 100 meters and 500 meters above the surface and run backward from

the site for 72

- hours. These individual back trajectories for 20 percent worst days in 2002 were also useful in evaluating model performance for individual days at the Class I areas.

7.5.2 Residence Time Plots

The next step was to plot residence time for each Class I area using five years of back trajectories for the 20 percent worst visibility days in 2000

- 2004. Residence time is the frequency that winds80 pass over a specific geographic area on the path to a Class I area. Separate residence time plots were generated using trajectories with 100m and 500m start heights. As illustrated in Figure 7.5.2, winds influencing Everglades on the 20 percent worst days co me from directions that move over either highly populated areas or areas where large poin t sources of emissions eminate.

Figure 7.5.2. Example residence time plot for 20 percent worst visibility days in 2000

- 2004

for Everglades . Based on trajectories w ith 100m start height.

7.5.3 SO 2 Areas of Influence

The next step was to develop sulfate extinction weighted residence time plots to define the geographic area with highest probability of influencing the receptor on the 20 percent worst days

in 2000

- 2004 that were dominated by sulfate. Each back trajectory was weighted by sulfate extinction for that day. This allows us to focus on the 20 percent worst days that are influenced by sulfate and place less importance on days influenced by organic carbon from fires. Sulfate

weighted back trajectories for the 20 percent worst days were combined for 5 years of data. The resulting sulfate extinction

- weighted residence time plots were used to define the geographic

Area of Influence for sources of SO 2 emissions. In Figure 7.5.3 the area representing 10 percent or greater residence time is outlined in red and the area representing 5 percent or greater81 residence time is outlined in gray. The VISTAS states focused their analyses on the Area of Influence defined by 5 percent or greater sulfate extinction

- weighted residence time.

Figure 7.5.3. Example SO 2 Area of Influence plot for sulfate extinction weighted residence time for 20 percent worst visibility days in 2000

- 2004 for Everglades . Based on trajectories with 100m start height.

7.5.4 Emissions Sources within SO 2 Areas of Influence

Residence time plots were then combined with geographically

- gridded emission data based on the 2002 baseline and 2018 BaseG emissions inventories. Plots were generated for the Areas of Influence defined by trajectories with 100 m and 500 m start heights. As a way of incorporating the effects of transport, deposition, and chemical transformation of point source emissions along the path of the trajectories, these data were w eighted by 1/d, where d was calculated as the82 distance, in kilometers, between the center of the grid cell in which a source is located and the center of the grid cell in which the IMPROVE monitor is located. The distance

- weighted point

source SO 2 emissio ns are then combined with the gridded extinction

- weighted back

- trajectory residence times at a spatial resolution of 36

- km.

The final step was to combine the residence times and gridded emissions data in plots and data sets. The distance weighted (1/d) gridded point source SO 2 emissions were multiplied by the

total extinction

- weighted back

- trajectory residence times on a grid cell by grid cell basis. These results were then normalized by the domain

- wide total and displayed as a percentage. The analysis was done using both the 2002 and 2018 base year inventories.

Figures 7.5.4

- 1 illustrates 2002 and 2018 distance weighted gridded emissions * sulfate extinction weighted residence time plots for Everglades . These maps help visualize where the

emissio ns reductions will be occurring between 2002 and 2018. The change in SO 2 emissions between 2002 and 2018 can be seen by comparing emissions source strengths in the two plots. Note the emissions from each source are normalized by the total emissions in th e domain.

Sources that reduce SO 2 emissions by 2018 will show a lower contribution to emissions in the domain. On the 2018 map the grid cells with these sources will show a lighter color gradient than on the 2002 map.

Figure 7.5.4

- 1. Everglades 2002 and 2018 SO 2 distance weighted emissions * SO4

extinction

- weighted residence time plots. 

## Comparison 2002 vs 2018 SO2 Emissions * Residence

Time Using 100 m Start Height



## – Everglades, FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 37%83 Figures 7.5.4

- 2 through 7.5.4.4 illustrate gridded SO 2 distance weighted emissions * sulfate extinction weighted residence time plots for 2002 and 2018 emissions for Chassahowitzka, St. Marks, Okefenokee, and Breton . These plots illustrate the relative importance of Florida sources of SO 2 compared to sources in neighboring states.

These analyses are serving as the basis for consultation among the VISTAS states.

Figure 7.5.4

- 2. 2002

- 2018 SO 2 distance weighted emissions * SO4 extinction

- weighted residence time plot for Chassahowitzka

. 

### Comparison 2002 vs 2018 SO2 Emissions * Residence

Time Using 100 m Start Height



### – Chassahowitzka , FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 68%84 Figure 7.5.4

- 3. 2002

- 2018 SO 2 distance weighted emissions * SO 4 extinction

- weighted residence time plot for St. Marks

. 

### Comparison 2002 vs 2018 SO2 Emissions * Residence

Time Using 100 m Start Height



### – St. Marks, FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 22%85 Figure 7.5.4

- 4. 2002

- 2018 SO 2 distance weighted emissions * SO 4 extinction

- weighted residence time plot for Okefenokee, GA

. 

## 2002 vs 2018 SO2 Emissions weighted by Residence Time
Okefenokee, GA

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 25%86 Figure 7.5.4

- 5. 2002

- 2018 SO 2 distance weighted emissions * SO 4 extinction

- weighted residence time plot for Breton, LA.

7.5. 5 Specific Source Types in the Areas of Influence for Florida and Nearby Class I Areas

As previously concluded in section 7.4, the greatest opportunity to affect visibility improvement

is to reduce emissions of SO 2 from stationary point sources . The fol lowing tables quantify the

emissions from specific source categories that emit SO 2 within the area of influence, defined as within the area having a residence time of 5% or greater surrounding each Class I area. The contributions from major source categor ies to the 2018 Base G2 inventory for the SO 2 Areas of

Influence for the Florida Class I areas are listed in Tables 7.5.5

- 1 through 7.5.5

- 5. 

### 2002 vs 2018 SO2 Emissions weighted by Residence Time
Breton, LA

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 10%87 Annual 2018 G2 Emissions

- Everglades Area of Influence
Category VOC NOX CO SO 2 PM

- 10 PM

- 2.5 NH 3

Fuel Comb. Elec. Util. 0% 15% 1% 34% 3% 5% 6%

Fuel Comb. Industrial 1% 8% 2% 11% 4% 11% 0%

Fuel Comb. Other 1% 4% 0% 9% 2% 4% 0% Chemical & Allied Product Mfg 0% 0% 0% 36% 0% 0% 3%
Metals Processing 0% 0% 0% 1% 0% 0% 0%

Petroleum & Related Industries 0% 0% 0% 0% 0% 0% 0%

Other Industrial Processes 3% 1% 0% 1% 2% 2% 0%

Solvent Utilization 40% 0% 0% 0% 0% 0% 0% Storage & Transport 10% 0% 0% 0% 0% 1% 0%
Waste Disposal & Recycling 1% 0% 1% 0% 2% 5% 1%
Highway Vehicles 21% 34% 41% 1% 2% 3% 37%

Off

- highway 20% 34% 45% 5% 3% 9% 0%

Miscellaneous 3% 3% 10% 2% 82% 60% 52%

Table 7.5.5

- 1. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Everglades

.

Annual 2018 G2 Emissions

- Chassahowitzka Area of Influence

Category VOC NOX CO SO 2 PM

10 PM

2.5 NH 3 Fuel Comb. Elec. Util. 0% 19% 1% 48% 3% 5% 3%

Fuel Comb. Industrial 0% 13% 1% 17% 2% 5% 0%

Fuel Comb. Other 2% 4% 1% 7% 1% 3% 0% Chemical & Allied Product Mfg 1% 1% 0% 12% 0% 1% 1%
Metals Processing 0% 0% 0% 4% 0% 0% 0%

Petroleum & Related Industries 0% 0% 0% 5% 0% 0% 0%

Other Industrial Processes 5% 4% 1% 4% 7% 11% 3%

Solvent Utilization 36% 0% 0% 0% 0% 0% 0% Storage & Transport 9% 0% 0% 0% 0% 0% 0%
Waste Disposal & Recycling 4% 3% 7% 0% 9% 19% 0%
Highway Vehicles 19% 27% 37% 1% 1% 1% 18%

Off

- highway 17% 25% 31% 2% 2% 4% 0%

Miscellaneous 6% 5% 20% 2% 73% 51% 75%

Table 7.5.5

- 2. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for

Chassahowitzka

.88 Annual 2018 G2 Emissions

- St. Marks Area of Influence

Category VOC NOX CO SO 2 PM

10 PM

2.5 NH 3 Fuel Comb. Elec. Util. 0% 20% 1% 46% 4% 7% 2%

Fuel Comb. Industrial 1% 17% 2% 20% 3% 5% 0%

Fuel Comb. Other 2% 4% 1% 8% 1% 3% 0% Chemical & Allied Product Mfg 1% 1% 0% 7% 0% 1% 1%
Metals Processing 0% 0% 0% 4% 0% 0% 0%

Petroleum & Related Industries 2% 0% 0% 5% 0% 0% 0%

Other Industrial Processes 7% 7% 1% 8% 8% 11% 4%

Solvent Utilization 36% 0% 0% 0% 0% 0% 0% Storage & Transport 8% 0% 0% 0% 1% 1% 0%
Waste Disposal & Recycling 4% 2% 7% 0% 8% 18% 0%
Highway Vehicles 18% 21% 36% 0% 1% 1% 18%

Off

- highway 14% 24% 28% 2% 2% 4% 0%

Miscellaneous 7% 4% 23% 1% 71% 50% 75%

Table 7.5.5

- 3. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for St.

Marks

.

Annual 2018 G2 Emissions

- Okefenokee Area of Influence

Category VOC NOX CO SO 2 PM

10 PM

2.5 NH 3 Fuel Comb. Elec. Util. 0% 22% 1% 51% 7% 12% 1%

Fuel Comb. Industrial 1% 16% 2% 25% 3% 5% 0%

Fuel Comb. Other 3% 4% 1% 6% 2% 4% 0%

Chemical & Allied Product Mfg 1% 1% 0% 4% 0% 1% 0%
Metals Processing 0% 0% 0% 4% 0% 0% 0%

Petroleum & Related Industries 0% 0% 0% 0% 0% 0% 0%

Other Industrial Processes 7% 6% 1% 7% 8% 11% 2%

Solvent Utilization 39% 0% 0% 0% 0% 0% 0%
Storage & Transport 7% 0% 0% 0% 0% 0% 0%
Waste Disposal & Recycling 4% 3% 7% 0% 8% 16% 0%
Highway Vehicles 18% 25% 38% 1% 1% 1% 10%

Off

- highway 14% 19% 29% 1% 2% 3% 0%

Miscellaneous 6% 4% 20% 1% 68% 46% 86%

Table 7.5.5

- 4. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Okefenokee, GA

.89 Annual 2018 G2 Emissions

- Breton Area of Influence

Category VOC NOX CO SO 2 PM

10 PM

2.5 NH 3 Fuel Comb. Elec. Util. 1% 19% 1% 33% 3% 6% 1%

Fuel Comb. Industrial 2% 31% 4% 18% 8% 14% 0%

Fuel Comb. Other 4% 3% 2% 5% 2% 4% 0% Chemical & Allied Product Mfg 4% 4% 1% 24% 2% 3% 6%
Metals Processing 1% 1% 1% 4% 3% 5% 0%

Petroleum & Related Industries 4% 1% 1% 8% 1% 1% 0%

Other Industrial Processes 7% 5% 2% 5% 11% 13% 16%

Solvent Utilization 33% 0% 0% 0% 0% 0% 0% Storage & Transport 8% 0% 0% 0% 1% 1% 0%
Waste Disposal & Recycling 3% 1% 5% 0% 5% 10% 1%
Highway Vehicles 12% 13% 33% 0% 1% 1% 7%

Off

- highway 14% 20% 29% 2% 2% 4% 0%

Miscellaneous 6% 2% 21% 1% 62% 39% 69%

Table 7.5.5

- 5. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Breton, LA

.

T he next step in the analysis was to determine which particular sources to target for potential further reasonable progress emission reductions. Lists of SO 2 point sources within the Areas of Influence for each Class I areas were developed using the most current (BaseG) VISTAS 2002 base year and 2018 future year emissions. For this purpose the Area of Influence was defined as the counties with maximum sulfate extinction weighted residence time greater than 5. For SO 2 sources within each Area of Influence, the following attributes were defined for eac h individual

unit:

**•** State, county, and source (plant), and industry identification codes

**•** SO 2 emissions for 2002 and 2018

**•** 2018 control efficiency

**•** Distance to Class I areas (defined by centroid of the Class I area)

**•** Emissions divided by distance (Q/d), a metr ic that accounts for the dispersion of

emissions over distance

**•** Maximum sulfate extinction weighted residence time (RTmax)

Based on this information, we could establish an objective criteria for identifying the sources of

SO 2 having the greatest potential for visibility impairment in each Class I area. The metric that most states have used to best summarize the relative visibility impact is Q/d * RTmax, where Q represents the amount of emissions (the greater the emissions, the greater the chance of visibi lity impairment), d is the distance (the greater the distance the more dispers e the emissions and the less impact on visibility), and RTmax

– a measure of the probability that the local climatology

wi ll direct winds from the source to the Class I area (lar ger RTmax leads to greater visibility impairment). Based on this product, associated with each unit, most states paired

- down the list of visibility

- impairing sources to those having at least a 1% relative contribution to the total Q/d*RTmax for a given Cl ass I area. Some states used a value of 0.5% relative contribution. In90 most cases, other states, in developing these data used the projected 2018 emissions, which for

EGU’s was based on the IPM electric generation demand model. The IPM model produced wh at we believe to likely be inaccurate projections for oil

- fired EGU’s in Florida. Namely, IPM projects that all of these units will convert to natural gas by 2018, thus producing essentially no

SO 2 emissions. Thus, for the purpose of identifying sources for potential reasonable progress review, we used the 2002 emissions. These data were used to develop the Florida

- specific criteria to determine sources for reasonable progress four

- factor review.

7.6 Reasonable Prog r ess Requirement

The Clean Air Act (Sec. 169A) requires that states provide for reasonable progress toward attaining the goal of preventing any future, and remedying any existing impairment of visibility in mandatory class I Federal areas. In determining reasonable progress, states sha ll consider the

four factors listed below.

**•** the costs of compliance

**•** the time necessary for compliance

**•** the energy and non

- air quality environmental impacts of compliance, and

**•** the remaining useful life of any potentially affected sources.

Regulations implem enting this requirement have been promulgated in 40 CFR Part 51, Subpart P

– Protection of Visibility. Section 51.308 specifically addresses the regional haze program requirements. Guidance on implementing the reasonable progress requirements have been

d eveloped by the U.S. EPA, “Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” revised June 1, 2007.

It is the stated objective of the regional haze rule that states shall establish a reasonable progress goal that provide s for “reasonable progress” to be made with in the planning period for each Class I area toward the goal of natural visibility conditions by 2064. Further, in establishing whether reasonable progress is being made, states must consider the straight line path fro m baseline (2000

- 2004) to natural conditions in 2064 (i.e., the uniform rate of progress )

. In establishing the reasonable progress goal for a Class I area, states must consider the four factors

listed above. In judging the adequacy of visibility improveme nt, the state first considers all current regulations and programs, and then determines the amount of additional improvement that may be necessary to represent reasonable progress. As stated in EPA’s guidance, “Given the significant emissions reductions t hat we anticipate to result from BART, the CAIR, and the implementation of other CAA programs, including the ozone and PM2.5 NAAQS, for many States this [determining the visibility improvement from these reductions] will be an important step in determining your RPG, and it may be all that is necessary to achieve reasonable progress in the first planning period for some States.” In fact, we assert this is true for Florida

, that the sum of all the existing regulations, including CAIR and the proposed Boiler MACT will result in “reasonable progress” of visibility improvement in all of the Florida Class I areas.91 Because of the large amount of emissions reductions of SO 2 and NOx in the Southeastern U.S. from existing rules and programs, Class I areas in VIS TAS states are expected to make considerable visibility improvement during the first regional haze planning period.

7.7 Florida’s Reasonable Progress Analysis

As stated earlier, Florida participated in regional planning through the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) regional planning organization. VISTAS was established to complete common technical requirements of the regional haze SIP, such as air quality modeling and data analysis. Through this process, VISTAS provided states data and information related to the state’s determination of reasonable progress toward the long

range visibility goal. This information helped states decide whi ch pollutants and which sources of these pollutants best provided for reasonable progress. Further, limited information was gathered to help states address the cost of potential pollutant reductions. While VISTAS provided a common dataset for states to u se in their reasonable progress requirement, the states did not attempt to establish a common procedure that all states would use. Instead, each state developed its own analysis of these data, considering state

- specific situations, to develop its

reasonab le progress requirements.

Focus on Sulfur Dioxide

VISTAS, through its modeling contractors, completed a series of sensitivity runs that evaluated visibility improvements to pollutant reductions. These runs showed reduction of sulfur dioxide (SO 2 ) prov ided the greatest benefit to visibility improvement throughout the VISTAS domain (See Figures 7.4.1 through 7.4.4)

. The VISTAS modeling contractors also evaluated the visibility improvement expected (projected) to occur as a result of the already implemen ted (on

the

- books) regulations by 2018 for each of the Class I areas. Because of the large decreases in

SO 2 , primarily from CAIR, most of the Class I areas easily show visibility improvement at a
faster rate than that of a straight line path to meeting th e national goal. Class I areas located along the coast show visibility improvement near the straight line path, sometimes marginally

better or marginally worse. The Florida Class I areas are in this category (see Figures 7.2.4

- 1 through 7.2.4

- 5). Becaus e considerable progress (visibility improvement) was expected by 2018

with on

- the

- books regulations, and because SO 2 reductions provide the best opportunity for additional improvement, the VISTAS states (including Florida) concluded that it was appropriate to consider only SO 2 reductions in any reasonable progress analysis.

An analysis of the source categories where SO 2 emissions are emitted, show that electric generating units and other industrial boilers that burn coal and oil represent approximately 95 percent of the emissions. As a result, VISTAS states, including Florida, decided to limit the reasonable progress analysis to these source categories.

Implementation of Reasonable Progress in Florida92 Beginning with the limitation to consider only SO 2 f rom electric generation and other industrial sources, Florida considered how to best apply the reasonable progress requirements. Through VISTAS, a common analysis was completed to identify the area of influence (AOI) surrounding each Class I area (see sec tion 7.5). This analysis considered three variables that each play a strong role in determining the impact any source may have on a particular Class I area. The first

variable is the amount of SO 2 emissions ; t he greater the emissions, the more likely a s ource may impact visibility. The second variable is distance to a Class I area ; t he farther you get from a source, the less the impact (acknowledging that chemistry builds in a delay). And the third variable is frequency of winds (residence time) in the direction of the Class I area from the source (trajectory analysis). This is a surrogate for the probability that winds will blow toward a Class I area. VISTAS contractors provided states a spreadsheet containing emissions, distance,

and residence time v alues for each source

- Class I area combination. The states then engaged in considerable discussion on how to best use this information to identify a list of sources that would be considered for a reasonable progress four

- factor analysis.

The VISTAS state s considered a number of different combinations of these variables as a surrogate for visibility impact. Using the notation that Q represents SO 2 emissions in tons per year, d represents distance in kilometers, and RTmax represents the maximum residence t ime for

a source

- Class I area, states considered the following metrics, all of which logically relate to potential visibility impairment:

Q/d*RTmax, Q*RTmax, Q/d, Q/d**2* RTmax

There were very little data or modeling results to provide a definitive anal ysis identify ing the best performing surrogate. Most VISTAS states made a judgment that Q/d*RTmax represented a reasonable surrogate for visibility impact. The purpose for formulating such a surrogate was to pair

- down the list of all stationary sources o f SO 2 to a shorter list representing the sources of greatest potential impact to Class I areas . This list would limit the number of sources having to undergo a four

- factor analysis, while providing the greatest potential benefit to visibility improvement. To limit the list, VISTAS considered a unit

- specific threshold of 1 percent of the Q/d*RTmax relative contribution considering all sources within the area of influence of each Class I area to be appropriate for identifying the largest contributors of visi bility impairment.

For reasons of better public clarity and understanding, we chose to develop a reasonable progress source selection metric based on Q/d that would be essentially equivalent to the nominal VISTAS metric of Q/d *RTMax. The Q/d metric wa s, in fact, used previously by VISTAS states in providing a cutpoint for when the VISTAS contractors would perform CALPUFF BART exemption modeling for relatively small sources. The cutpoint used was Q/d = 10, a value that a

limited amount of evaluation mo deling showed a reasonable probability the visibility impacts would not be significant (i.e., < 0.5 dv). Since visibility in Class I areas in or near Florida is expected to improve at very near the nominal straight line path to the 2064 goal with current rules, we chose a cutpoint of Q/d = 50 (five times the nominal significance criteria). This assures us that many of the largest sources of SO 2 nearest the Class I areas are required to address reasonable progress , while smaller sources (not expected to pr ovide significant, cost

- effective reductions) are excluded. Similarly, we provided some bounds for emissions (Q) and distance (d) to affect a reasonable process. First, we exclude small (< 250 tons per year) units, the93 rationale being that any reduction s would be very small and likely not very cost effective. Second, we do not consider any sources outside of 300 kilometers from a Class I area. This limitation is consistent with the bounds used in the BART exemption analysis where only

sources within th is distance from a Class I area were considered. Third, we only consider sources which commenced construction or submitted a complete application prior to August 30, 1999, a date after which permit review requires that visibility specifically be addressed (EPA’s Regional Haze Regulations, July 1, 1999)

. Finally, we use the 2002 emissions for Q, where VISTAS used the projected 2018 emissions. This is important in Florida for two reasons. First, we question some of the IPM projections that convert all of our oil

- fired boilers to natural gas. If we accepted this premise, emissions of SO 2 are presumed to be zero in 2018 and these units

would not be considered for reasonabl e progress review . Second, as a general strategy, we don’t

want to base our selection of sources for four

- factor review on IPM’s guess at how the CAIR

market

- based reductions will occur. We believe it is best to start with the known largest sources

havin g the potential to impair visibility and make sure that these sources are addressed through

the reasonable progress process.

The Florida criter ion (Q/d>=50) captures for reasonable progress analysis the 1 st through 9 th

, 15 th , 18 th , 19 th , 27 th , and 30 th la rgest SO 2 sources (2002) in the state. Of all the SO 2 sources considered in the VISTAS area of influence around each of the Class I areas, the Florida

criterion subjects 67.6 percent of the emissions to reasonable progress review. In comparison,

use of t he criteria used by VISTAS (1% contribution using Q/d*RTmax) would select 70.5 percent of the emissions.

TPY Percent of

Total

2002 Total Florida SO 2 Emissions (within VISTAS AOI) 497,867

Selected w/1% criteria 351,167 70.5%

Selected w/ Q/d >50 criteria 336,595 67.6%

Consequently, we believe that the Florida criterion is consistent with the general approach taken by the VISTAS states. A complete comparison of the Florida selection criteria with the general

VISTAS criteria is contained in the tables 7.7.1

– 7.7.4

.949596 CAIR and Proposed Boiler MACT as Representing Reasonable Progress

Many of the emission units subject to reasonable progress analysis , as defined above, either have already reduced SO 2 emissions or will be reducing SO 2 e missions soon

. These reductions have come about from company decisions to shut

- down or re

- power certain units, or to install new control equipment (scrubbers) in response to the CAIR regulations. In addition, on December 2, 2011, USEPA proposed its recon sideration of the boiler MACT standards , originally proposed March 21, 2011 . This rule will require reduction in acid gases which will have the co

- benefit of reducing SO 2 emissions either through the use of scrubbers or fuel switching. The RockTenn facility in Fernandina Beach (formerly Smurfit

- Stone and before that Jefferson Smurfit) , one of the listed reasonable progress sources subject to the four

- factor analysis, is subject to the Boiler

MACT rule. In the case where sources are responding to CA IR, the department is engaging these sources to commit to the continuous operation of these control equipment, where CAIR would not have this requirement. Table 7.7.5 shows source units subject to reasonable progress

review and their current SO 2 emissions in comparison to the baseline emissions

. As can be seen,

SO 2 reductions are well underway to meeting, and exceeding, the amount needed to reach the 2018 projection. While SO 2 reductions due to the original Boiler MACT rule are included in the 2018 proje ction, the revised Boiler MACT rule is expected to result in even greater reductions. USEPA estimates that 340,000 tons of SO 2 would be reduced nationwide

. Table 7.7. 6 lists the boilers and process heaters in Florida subject to the proposed federal rule.

All sources identified as subject to reasonable progress review are also subject to either CAIR or the proposed Boiler MACT. Both CAIR and the proposed Boiler MACT rule will result in significant SO 2 reductions in Florida. In sum, these reductions are estimated to exceed the reductions already evaluated as part of the 2018 projections presented in this plan.97 CAIR and Boiler MACT Meeting the Four

- Factor Analysis Requirement

For EGUs subject to CAIR, the EPA evaluated a number of factors, including the cost of compliance and the time necessary for compliance. In the CAIR rule, EPA determined “that the earliest reasonable deadline for compliance with the final highly cost effective control levels for reducing emissions was 2015 . . .” (70 FR 25197

- 2 5198, May 12, 2005)

. Florida believes that the cost of compliance and the time necessary for compliance are the dominant factors for determining if additional reductions would be reasonable from CAIR sources. Based on detailed analysis in the May 12, 2005, CAIR rule, Florida concludes that CAIR controls satisfy reasonable progress for SO 2 for the first planning period ending in 2018.

Similarly, for sources subject to the boiler MACT rule, the EPA evaluated the reduction in emissions of air pollutants taking into consideration the cost of achieving such emission reductions and any non

- air quality and energy requirements. In addition, the time for compliance is to be within three years of the final rule, which is projected to be the end of 2013

. While the boiler MACT analysis considered acid gases and toxic emissions like mercury, the control of acid gases is associated with the control of SO 2 and thus provides complementary reductions. In fact, EPA states in its proposal

“Finally, we considered the fact that setting conventional MACT standards for HCl a s well as PM (as a surrogate for metal s including manganese) would result in significant reductions in emissions of other pollutants, most notably SO 2 , non

- condensable PM, and

other non

- HAP acid gases (e.g., hydrogen bromide) and would likely also result in additional reductions in emissions of mercury and other HAP metals (e.g., selenium).

The additional reductions of SO 2 alone attributable to the proposed MACT standard for

HCl are estimated to be 340 ,000 tons per year in the third year following promulgation of the proposed HCl standard.”

It should also be noted that the Guidelines for BART Determinations Under the Regional Haze

Rule suggests that MACT standards will likely be more stringent than BAR T determinations. Since many of the BART and reasonable progress factors are the same, it follows that MACT can be applied for reasonable progress assessments.

Given that both the CAIR and the Boiler MACT emission reduction requirements were develop ed using processes similar to the regional haze four

- factor approach, it is reasonable to accept that sources that must comply with either rule satisfy the reasonable progress requirements. Further, s ince the visibility improvement in all of the Class I a reas in and adjacent to Florida are consistent with the nominal straight

- line path to achieving the national visibility goal by 2064, Florida asserts that reasonable prog r ess is met for the subject sources with the combination of CAIR and Boiler MACT requi rements.98 Table 7.7.5 Reasonable Progress Sources

Plant ID Plant Name Point ID Type 2002 SO2

EMISSION (TON/YR) 2010 SO2

EMISSION (TON/YR) Existing and Projected SO2 Control Equipment Disposition

Currently Shutdown

570040 TECO GANNON 1 coal-fired boiler 5,157 0 Shutdown (2003) Provide documentation

2 coal-fired boiler 4,942 0 Shutdown (2003) Provide documentation

3 coal-fired boiler 5,602 0 Shutdown (2003) Provide documentation

4 coal-fired boiler 5,577 0 Shutdown (2003) Provide documentation

5 coal-fired boiler 8,043 0 Shutdown (2003) Provide documentation

6 coal-fired boiler 16,097 0 Shutdown (2003) Provide documentation

1030011 PE BARTOW 1 oil/gas-fired boiler 6,483 0 Shutdown (2009) Provide documentation

2 oil/gas-fired boiler 6,149 0 Shutdown (2009) Provide documentation

3 oil/gas-fired boiler 11,249 0 Shutdown (2009) Provide documentation

Proposed Shutdown

110036 FPL PORT EVER 3 oil/gas-fired boiler 6,409 1,798 To be Shutdown (2013) Provide documentation

4 oil/gas-fired boiler 6,205 2,003 To be Shutdown (2013) Provide documentation

Subject to BART

250003 FPL TURKEY POINT 1 oil/gas-fired boiler 4,610 1,595 Subj. to BART -satisfies RP

2 oil/gas-fired boiler 4,327 1,576 Subj. to BART -satisfies RP

170004 PE CRYSTAL RIVER 1 coal-fired boiler 18,998 14,874 Subj. to BART -satisfies RP

2 coal-fired boiler 20,728 18,176 Subj. to BART -satisfies RP

0310045-B JEA NORTHSIDE 3 oil/gas-fired boiler 7,146 912 Subj. to BART -satisfies RP

330045 GULF POWER CRIST 7 coal-fired boiler 21,546 2,239 Subj. to BART -satisfies RP

810010 FPL MANATEE 1 oil/gas-fired boiler 13,930 3,481 Subj. to BART -satisfies RP

2 oil/gas-fired boiler 15,073 3,998 Subj. to BART -satisfies RP

1010017 PE ANCLOTE 1 oil/gas-fired boiler 13,879 3,325 Subj. to BART -satisfies RP

2 oil/gas-fired boiler 13,225 3,740 Subj. to BART -satisfies RP

4-Factor Analysis

10006 GRU DEERHAVEN 5 coal-fired boiler 6,969 3,161 Dry Scrubber 4-factor analysis

170004 PE CRYSTAL RIVER 3 coal-fired boiler 26,436 1,660 Wet Scrubber 4-factor analysis

4 coal-fired boiler 24,635 4,730 Wet Scrubber 4-factor analysis

0310045-A JEA ST JOHNS RIVER 16 coal-fired boiler 11,076 4,786 Wet Scrubber 4-factor analysis

17 coal-fired boiler 10,185 4,616 Wet Scrubber 4-factor analysis

0310045-B JEA NORTHSIDE 27 coal-fired boiler 5,090 1,584 Dry Scrubber No longer applicable

530021 FL CRUSHED STONE 18 coal-fired boiler 2,906 2,197 Dry Scrubber No longer applicable

1050004 LAKELAND MCINTOSH 6 coal-fired boiler 6,994 4,198 Wet Scrubber 4-factor analysis

1070025 SEMINOLE ELECTRIC 1 coal-fired boiler 10,912 8,483 Wet Scrubber 4-factor analysis

2 coal-fired boiler 12,775 8,477 Wet Scrubber 4-factor analysis

Non-EGU Boiler MACT

890003 Rock Tenn 15 coal-fired boiler 3,242 3,344 RP via boiler MACT

336,595 104,95399 Table 7.7.6 - Florida Units Subject to the Industrial, Commercial, and Institutional Boilers and Process Heaters Rule (Boiler MACT)

FacilityID UnitID Classification Design Capacity (mmBtu/hr) MACT Fuel

Category

FLACHFood BOILER NO. 1 Boiler 100 Liquid

FLACHFood BOILER NO. 3 Boiler 100 Gas 1

FLACHFood DV3 HEATEC Process Heater <10 Gas 1

FLCFRC252 Boiler #3 (McBurney) Boiler 85 Biomass

FLCFRC252 Boilers #4 & #5 (Hurst) Boiler 29.9 Biomass
FLFLPowerandLight Aux Boiler Boiler 15.032 Gas 1

FLFPLLauderdale Aux Boiler Boiler 15.5 Gas 1

FLFPLMartin PMG Aux Boiler Boiler 16.3 Gas 1

FLGPHawthorne EU 001 Wood Fired Boiler Boiler 224 Biomass

FLGPHosfordOSB 011-THERMAL OIL HEATER #1 Process Heater 40 Biomass

FLGPHosfordOSB 011-THERMAL OIL HEATER #2 Process Heater 40 Biomass

FLGPPalatka EU15 Boiler 540 Gas 1

FLGPPalatka EU16 Boiler 512.7 Biomass

FLGPPalatka EU44 Boiler 244.6 Gas 1
FLHighPerformance Heater Process Heater <10 Gas 1

FLIPPensacola Power Boiler 3 / EU ID 33 Boiler 347 Coal

FLIPPensacola Power Boiler 4 / EU ID 37 Boiler 666 Biomass

FLIPPensacola Power Boiler 5 / EU ID 02 Boiler 195 Gas 1

FLIPPensacola Power Boiler 6 / EU ID 03 Boiler 535 Gas 1

FLMOSAIC263 Auxiliary Process Steam Boiler (EU 055) Boiler 93.2 Gas 1

FLMOSAIC263 Package Watertube Boiler (EU 051) Boiler 64 Gas 1

FLMosaicFertilizerRiverview264 Auxiliary Steam Boiler (EU 043) Boiler 130 Gas 1

FLNavalAirStationJacksonville EC015 Boiler 1.69 Liquid

FLNavalAirStationJacksonville EC102 Boiler 6.3 Liquid

FLNavalAirStationJacksonville EU071 Boiler 13 Gas 1

FLNavalAirStationJacksonville EU072 Boiler 13 Gas 1

FLNavalAirStationJacksonville EU076 Boiler 13 Gas 1

FLNavalAirStationJacksonville EU079 Boiler <10 Gas 1

FLNavalAirStationJacksonville EU087 Boiler <10 Gas 1

FLNavalAirStationJacksonville EU088-1 Boiler 25.11 Gas 1

FLNavalAirStationJacksonville EU088-2 Boiler 25.11 Gas 1

FLNavalAirStationJacksonville EU088-3 Boiler 10.1 Gas 1

FLNavalAirStationJacksonville EU089-1 Boiler 14.65 Gas 1

FLNavalAirStationJacksonville EU089-2 Boiler 14.65 Gas 1

FLNavalAirStationJacksonville EU090 Boiler 14.65 Gas 1

FLNavalAirStationJacksonville EU87-1 Boiler 31.4 Gas 1

FLNavalAirStationJacksonville EU87-2 Boiler 31.4 Gas 1

FLNavalAirStationJacksonville EU87-3 Boiler 31.4 Gas 1

FLNorthFloridaLumber 3-3900-150 Boiler 23 Biomass

FLNorthFloridaLumber 3-5700-200 Boiler 2.4 Biomass

FLNorthFloridaLumber 4-5700-01 Boiler 42.9 Biomass

FLNorthFloridaLumber HYB3948-150-7 Boiler 1.8 Biomass

FLOkeelanta268 Boiler No. 16 Boiler 211 Liquid

FLOsceolaFarms Boiler No. 2 Boiler 280 Biomass

FLOsceolaFarms Boiler No. 3 Boiler 292 Biomass

FLOsceolaFarms Boiler No. 4 Boiler 280 Biomass

FLOsceolaFarms Boiler No. 5 Boiler 330 Biomass

FLOsceolaFarms Boiler No. 6 Boiler 379 Biomass
FLPepperidgeFarm HOT WAT GAS FIRED Process Heater <10 Gas 1
FLPepperidgeFarm SML Gas Fired Boiler <10 Gas 1

FLPLBartowPowerPlantStPetersb EU004 Boiler 15.5 Gas 1
FLRayonierPerformance PB06 Boiler 525 Biomass

FLSACBranford Raw Mill Air Heater Process Heater 32 Gas 1

FLSmurfit-Stone 5PB Boiler 805 Biomass

FLSmurfit-Stone 7PB Boiler 1021 Coal

FLSolutiaIncPensacola B-4 Boiler 241 Liquid

FLSolutiaIncPensacola B-5 Boiler 241 Liquid

FLSolutiaIncPensacola B-6 Boiler 241 Liquid

FLSolutiaIncPensacola B-7 Boiler 388 Gas 1

FLSolutiaIncPensacola B-8 Boiler 388 Gas 1

FLSolutiaIncPensacola B-9 Boiler 344 Gas 1

FLSolutiaIncPensacola H2 Plant Process Heater 180 Gas 1

FLSolutiaIncPensacola V-1 Process Heater 10.5 Gas 1

FLSolutiaIncPensacola V-2 Process Heater 10.5 Gas 1

FLSolutiaIncPensacola V-3 Process Heater 10.5 Gas 1

FLSolutiaIncPensacola V-4 Process Heater 16.9 Gas 1

FLSolutiaIncPensacola V-5 Process Heater 16.9 Gas 1

FLSolutiaIncPensacola V-6 Process Heater 16.9 Gas 1

FLSolutiaIncPensacola V-7 Process Heater 26 Gas 1

FLSolutiaIncPensacola V-8 Process Heater 28 Gas 1

FLStRegis 040-A Process Heater 56.7 Gas 2

FLStRegis 040-B Process Heater 56.7 Gas 2

FLStRegis 040-C Process Heater 56.7 Gas 2

FLStRegis 041-A Process Heater 28.4 Gas 2

FLStRegis 041-B Process Heater 28.4 Gas 2

FLStRegis 041-C Process Heater 28.4 Gas 2

FLStRegis 042 Process Heater 55.65 Gas 2

FLSugarCaneGrowersCoop Boiler No. 1 Boiler 266.7 Biomass

FLSugarCaneGrowersCoop Boiler No. 2 Boiler 269 Biomass

FLSugarCaneGrowersCoop Boiler No. 3 Boiler 229 Biomass

FLSugarCaneGrowersCoop Boiler No. 4 Boiler 572 Biomass

FLSugarCaneGrowersCoop Boiler No. 5 Boiler 439.1 Biomass

FLSugarCaneGrowersCoop Boiler No. 8 Boiler 504 Biomass

FLUSSugarCorp Boiler No. 1 Boiler 495 Biomass

FLUSSugarCorp Boiler No. 2 Boiler 447 Biomass

FLUSSugarCorp Boiler No. 4 Boiler 633 Biomass

FLUSSugarCorp Boiler No. 7 Boiler 812 Biomass

FLUSSugarCorp Boiler No. 8 Boiler 1185 Biomass100 Applicability of Florida Crushed Stone and JEA Northside

Two units have taken federally enforceable permit conditions that limit SO 2 emissions such that they are no longer applicable to reasonable progress review.

Central Power and Lime is a coal

- fired power boiler co

- located with the Florida Crushed Stone (now CEMEX) cement plant. This unit has received a construction permit to convert the coal

fired boiler to a biomass boiler, and is prohibited from burning coal once it makes the appropriate changes to the boiler to burn biomass. Ultra low sulfur distillate fuel oil will be used

for start

- up, shutdown, and bed stabilization. The commitment s to execute these changes are

contained in t he federally enforceable construction permit, the essential parts of which are included in Exhibit 2. The result of these changes is that the source is no longer subject to the reasonable progress review. The maximum allowed SO 2 emissions are now set at 5 91.3 tons per year. The distance to the nearest Class I area (Chassahowitzka NWA) is 22.4 kilometers. This results in a Q/ d of 26.4, well below the applicability criterion of Q/ d = 50 or greater. Thus, no further analysis of this source is required. Florida Crushed Stone

- Central Power and Lime

Circulating Fluidized Bed Boiler 1 (emissions unit 27) is a coal, coal coated with latex, petroleum coke, and landfill gas fired circulating fluidized bed (CFB) boiler rated at 298 MW. Sulfur dioxide emissions are controlled by the injection o f limestone into the CFB boiler beds.

Residual SO 2 emissions are further controlled by the use of an add

- on spray dryer absorber system. The federally enforceable SO 2 emission limitation is 0.2 lb/MMBtu, 24

- hour average, and 0.15 lb/MMBtu, 30

- day rolling average. Annual emissions are thus limited to 1,816 tons

SO 2 per year. JE A Northside

Since 2002, the year for which data is used to determine applicability for reasonable progress review, permit conditions have been imposed on this unit that limit the annual emission s of SO 2

. In 2009, JEA requested and received the permit limitations listed above. The new, federally enforceable limits result in the source no longer being subject to the reasonable progress review.

The JEA Northside source is located 69.3 kilometers from the closest Class I area (Okefenokee NWA). With maximum allowable SO 2 emissions of 1,816 tons, the applicability metric Q/ d equals 26.2, which is less than the Q/ d = 50 or greater criterion. Thus , no further analysis of this source is required.

Shut downs of Reasonable Progress Sources

Nine units subject to reasonable progress review are shutdown. This includes six units at the Tampa Electric Gannon facility, shutdown in 2003, and three units at the Progress Energy Bartow facility, shutdown in 2009. The permits requiring the shutdown of these sources are

included in Exhibit 2.

Two additional units subject to the reasonable progress review will soon be shutdown. Th ese are units 3 and 4 at the Florida Power and Light Port Everglades facility (to be shutdown in 2013). A copy of the department’s intent to issue a permit is included in Exhibit 2.101 7.8 Best Available Retrofit Technology (BART) for Individual Sources

Section 169A of the CAA directs States to assess certain large, older emission sources for additional controls in order to address visibility impacts. States are directed to conduct BART determinations for such sources in specific source categories, and which contribute to visibility impairment in Class I areas. The 1999 regional h aze rule includes the BART requirement, and

directs States to include BART in their regional haze SIPs. On July 6, 2005, USEPA published a revised final rule, including Appendix Y to 40 CFR Part 51, the Guidelines for BART Determinations Under the Regiona l Haze Rule (hereinafter referred to as the “BART Guidelines”) that provides direction to states on determining which of these sources should be subject to BART, and how to determine BART for each source.

A BART

- eligible source is one which has the potent ial to emit 250 tons or more of a visibility

impairing air pollutant, was put in place between August 7, 1962 and August 7, 1977, and whose operations fall within one or more of 26 specifically listed source categories. Under the CAA, BART is required for any BART

- eligible source that a State determines ‘‘emits any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of visibility in any

such area.’’

For those sources subject to BART, Section 169A(g)(7) of the CAA re quires that States must consider the following factors in making BART determinations: (1) the costs of compliance, (2) the energy and non

- air quality environmental impacts of compliance, (3) any existing pollution control technology in use at the source, ( 4) the remaining useful life of the source, and (5) the degree of improvement in visibility which may reasonably be anticipated to result from the use of such technology.

7.8.1 BART

- Eligible Sources in Florida

The following is a list of facilities wi th BART

- eligible sources in Florida

. The department

determined the list using information from the ARMS database, and modified through surveys sent to potential eligible sources and through public workshops held to develop the state BART

rule.102 FACILITY

ID OWNER COMPANY NAME SITE NAME CAIR

1 0010006 CITY OF GAINESVILLE, GRU DEERHAVEN GENERATING STATION Y

2 0050009 ROCKTENN ( STONE CONTAINER

CORPORATION ) PANAMA CITY MILL

3 0050014 GULF POWER COMPANY LANSING SMITH PLANT Y

4 0090006 FLORIDA POWER & LIGHT (PCC) CAPE CANAVERAL POWER PLANT Y

5 0090196 RELIANT ENERGY INDIAN RIVER, L.L.C. INDIAN RIVER PLANT

- RELIANT Y

6 0110036 FLORIDA POWER & LIGHT (PPE) PORT EVERGLADES POWER PLANT Y

7 0170004 FLORIDA POWER CORPORATION CRYSTAL RIVER POWER PLANT Y

8 0250003 FLORIDA POWER & LIGHT (PTF) TURKEY POINT POWER PLANT Y

9 0310045 JEA NORTHSIDE/SJRPP Y

10 0310071 IFF CHEMICAL HOLDINGS, INC. IFF CHEMICAL HOLDINGS, INC.

11 0330040 SOLUTIA INC. SOLUTIA INC.

12 0330042 INTERNATIONAL PAPER COMPANY PENSACOLA MILL

13 0330045 GULF POWER COMPANY CRIST ELECTRIC GENERATING PLANT Y

14 0470002 WHITE SPRINGS AGRICULTURAL CHEMICALS,INC WHITE SPRS AG CHEM

- SR/SC CMPLX

15 0510003 U.S. SUGAR CORP. CLEWISTON MILL CLEWISTON MILL AND REFINERY

16 0530010 CEMEX CEMEX

17 0570005 CF INDUSTRIES, INC., PLANT CITY PHOS PLANT CITY PHOSP

18 0570008 MOSAIC CARGILL

-- RIVERVIEW FACILITY

19 0570024 IMC PHOSPHATES COMPANY PORT SUTTON TERMINAL

20 0570039 TAMPA ELECTRIC COMPANY BIG BEND STATION Y

21 0610029 CITY OF VERO BEACH CITY OF VERO BEACH MUNICIPAL UTILITIES Y

22 0730003 CITY OF TALLAHASSEE ARVAH B.HOPKINS GENERATING STATION Y

23 0810010 FLORIDA POWER & LIGHT (PMT) MANATEE POWER PLANT Y

24 0850001 FLORIDA POWER & LIGHT (PMR) FPL / MARTIN POWER PLANT Y

25 0890003 ROCKTENN ( SMURFIT STONE CORPORATION (US) ) JSC

- FERNANDINA BEACH PAPERBOARD

MILL

26 0990016 ATLANTIC SUGAR ASSOCIATION ATLANTIC SUGAR MILL

27 0990019 OSCEOLA FARMS OSCEOLA FARMS

28 0990026 SUGAR CANE GROWERS CO

- OP SUGAR CANE GROWERS CO

- OP

29 0990042 FLORIDA POWER & LIGHT (PRV) RIVIERA POWER PLANT Y

30 0990045 LAKE WORTH UTIL. TOM G. SMITH PLANT Y

31 0990061 U.S.SUGAR CORP. BRYANT MILL U.S. SUGAR CORP. BRYANT MILL

32 1010017 FLORIDA POWER CORP. ANCLOTE POWER PLANT Y

33 1030011 FLORIDA POWER CORPORATION FPC

- BARTOW PLANT Y

34 1050003 LAKELAND ELECTRIC CHARLES LARSEN MEMORIAL POWER

PLANT Y

35 1050004 LAKELAND ELECTRIC C.D. MCINTOSH, JR. POWER PLANT Y

36 1050046 MOSAIC

- BARTOW BARTOW

37 1050053 MOSAIC

- GREEN BAY GREEN BAY PLANT

38 1050055 IMC PHOSPHATES COMPANY SOUTH PIERCE

39 1050059 MOSAIC

- NEW WALES NEW WALES

40 1070005 GEORGIA

- PACIFIC CORP. PULP/PAPER

MILL PALATKA

41 1070014 FLORIDA POWER & LIGHT (PPN) PUTNAM POWER PLANT Y

42 1110003 FT PIERCE UTILITIES AUTHORITY H D KING PWR PLNT Y

43 1130003 STERLING FIBERS, INC. STERLING FIBERS, INC.

44 1130005 EXXONMOBIL PRODUCTION COMPANY ST REGIS TREATING FAC AND JAY GAS

PLANT

45 1230001 BUCKEYE FLORIDA, LIMITED PARTNERSHIP PERRY

46 1290001 TALLAHASSEE CITY PURDOM GENERATING

STA. PURDOM GENERATING STATI Y103 The BART

- eligible sources were identified using the methodology in the BART Guidelines.

**•** One or more emissions units at the facility fit within one of the 26 categories listed in the BART Guidelines;

**•** The emission unit(s) were in existence on August 7, 1977 and began operation at some point on or after August 7, 1962; and

**•** The limited potential emissions from all emission units identified in the previous two bullets were greater than 250 tons per year o f any of these visibility

- impairing pollutants:

SO 2 , NOx, and PM10.

The BART Guidelines recommend addressing these visibility

- impairing pollutants: SO 2 , NO

x , and particulate matter, and suggest that States use their best judgment in determining whether t o address VOC or ammonia emissions. Florida addressed SO 2 and NOx, and used particulate matter less than 10 microns in diameter (PM 10 ) as an indicator for particulate matter to identify

BART

- eligible units, as the BART Guidelines recommend. As discussed in detail in Appendix

L , VISTAS modeling demonstrated that VOCs and ammonia from point sources are not significant for visibility impa irment . For this reason Florida did not evaluate emissions of VOCs

and ammonia in BART determinations.

7.8.2 Florida’s BART Rule

In support of the federal regional haze rule for the Best Available Retrofit Technology requirement, Florida developed state rule 62

- 296.340, Florida Administrative Code. In this rule, we adopt by reference the core of the federal rule, and include additional rule language to make certain decisions where the federal rule leaves options to the state. We also provide for a requi rement that BART

- eligible sources submit a state construction permit application. In this application, the source must complete the BART analysis required in the federal rule and

recommend the BART controls or emission limitations consistent with that pro cess. The state then makes the final BART determination and issues a construction permit making any changes federally enforceable.

The optional components or decisions in the state rule include the exclusion of volatile organic compounds (VOC) and ammon ia from BART review, accepting BART equal to CAIR for electric generating units (thereby limiting BART for EGU’s to PM only), providing for deminimus levels below which BART would not apply for that pollutant, and provides specific criteria for exemption f rom BART. A copy of the Florida BART rule is included in Exhibit 1

.

The USEPA has determined that, as a whole, the CAIR cap

- and

- trade program improves visibility more than implementing BART for individual sources in states affected by CAIR. A

State tha t opts to participate in the CAIR program under 40 CFR 96.201

- .224 (Subpart AAA through EEE) need not require affected BART

- eligible EGU to install, operate, and maintain

BART for SO 2 or NOx emissions. All (or nearly all) coal

- fired EGU’s in Florida are e xpected to

control for SO 2 and NOx in response to CAIR, and since Florida is participating in CAIR and accepts the USEPA’s overall finding that CAIR “substitutes” for BART for NOx and SO 2 , Florida’s EGUs were allowed to submit BART exemption modeling demon strations for PM emissions only. However, any EGU wishing to not rely on CAIR may submit demonstrations104 that consider SO 2 , NOx, and PM in making their exemption. All but two EGUs demonstrated that they do not contribute to visibility impairment in any Cla ss I area , with four of these BART

eligible EGU showing exemption considering SO 2 , NOx, and PM

. Of the two non

- exempt

BART

- eligible EGU sources, e ach has submitted a BART determination for PM.

7.8. 3 Determination of Sources Subject to BART in Florida

Under the federal Guidelines for Best Available Retrofit Technology Determinations (Appendix Y to Part 51, BART Guidelines )

, Florida may exempt sources from BART if they do not cause or contribute to visibility impairment in a Class I area. In accordance with the Florida BART rule exemptions

, and the federal BART Guidelines , two exemption criteria were used to

determine which BART

- eligible sources could be exempted from the BART determination process. The first is based on meeting a simple emissions crite rion applicable within two distances (50 km and 100 km) from any Class I area. Four of the BART

- eligible sources met this exemption. The second criterion is base on source

- specific analyses to determine which sources cause or contribute to visibility im pairment using the CALPUFF air quality model. A CALPUFF modeling protocol developed by VISTAS was used to assure consistent and adequate analysis and is included in Appendix L . In accordance with the Guidelines, and a Florida

specific review of appropria teness (see Appendix L), a contribution threshold of 0.5 deciviews was used for determining which sources were subject to BART. 3 1 of the BART

- eligible

sources met this modeling exemption.

Table 7.8. 3

- 1 lists the facilities that were able to demonstrat e exemption from BART based on CALPUFF modeling conducted using the VISTAS modeling protocol and the new IMPROVE equation. These modeling demonstrations were carefully reviewed by the department

, and were provided to the U.S. EPA Region 4 office and to t he federal land managers for their review and comment. Based on these reviews, a number of BART

- eligible sources had to correct and re

submit their demonstration modeling to comply with the modeling protocol. Six BART

- eligible sources made changes to the ir operation in order to meet the exemption criteria. These sources are Georgia Pacific

- Palatka, Smurfit

- Stone

- Fernandina Beach, Smurfit

- Stone

– Panama City, Mosaic New Wales, Mosaic Riverview, and CF Industries. All of these changes have been incorporated into their permits and are federally enforceable. Four of the BART

- eligible sources

that also are CAIR sources met the modeling exemption criterion based on consideration of SO 2 , NOx, and PM

. Thus, these sources do not rely on the CAIR

- bette r

- than

- BART assertion. The sources include: Gainesville Deerhaven, City of Vero Beach Utilities, Lake Worth Utilities Tom G. Smith, and Florida Power and Light Putnam. Additionally, one BART

- eligible source, Tampa Electric Company Big Bend, provided infor mation showing that the existing controls at the three subject units are the top level of control for each of the visibility pollutants, and, thus,

full BART review is not necessary

. Finally, documentation for the BART

- eligible units that

have shutdown is included in Exhibit 2.

Included in the table is the disposition of all the BART

- eligible sources.105 Table 7.8.3-1 BART-Eligible Sources and Their Disposition

FACILITY

ID O WNER CO MPANY NAME SITE NAME Full

BART Meets Modeling
Exemption Meets Model

Rule
Exempt. Shut

down Max Visib
Impact

(dv)

10006 CITY OF GAINESVILLE, GRU DEERHAVEN GENERATING STATION xxx 0.205

50009 STONE CONTAINER CORPORATION PANAMA CITY MILL p 0.498

50014 GULF POWER COMPANY LANSING SMITH PLANT xx 0.06

90006 FLORIDA POWER & LIGHT (PCC) CAPE CANAVERAL POWER PLANT x

90196 RELIANT ENERGY INDIAN RIVER, L.L.C. INDIAN RIVER PLANT - RELIANT xx 0.178

110036 FLORIDA POWER & LIGHT (PPE) PORT EVERGLADES POWER PLANT xx 0.462

170004 FLORIDA POWER CORPORATION CRYSTAL RIVER POWER PLANT c

250003 FLORIDA POWER & LIGHT (PTF) TURKEY POINT POWER PLANT c

310045 JEA NORTHSIDE/SJRPP xx 0.402

310071 IFF CHEMICAL HOLDINGS, INC. IFF CHEMICAL HOLDINGS, INC. x 0.269

330040 SOLUTIA INC. SOLUTIA INC. x

330042 INTERNATIONAL PAPER COMPANY PENSACOLA MILL x 0.057

330045 GULF POWER COMPANY CRIST ELECTRIC GENERATING PLANT xx 0.21

470002 WHITE SPRINGS AGRICULTURAL CHEMICALS WHITE SPRS AG CHEM-SR/SC CMPLX x

510003 U.S. SUGAR CORP. CLEWISTON MILL U.S. SUGAR CLEWISTON MILL AND REFINERY x 0.265

530010 CEMEX CEMEX x

570005 CF INDUSTRIES, INC., PLANT CITY PHOS CF INDUSTRIES, INC., PLANT CITY PHOSP p 0.498

570008 MOSAIC RIVERVIEW p 0.494

570024 IMC PHOSPHATES COMPANY PORT SUTTON TERMINAL x

570039 TAMPA ELECTRIC COMPANY BIG BEND STATION xxxx

610029 CITY OF VERO BEACH CITY OF VERO BEACH MUNICIPAL UTILITIES xxx 0.17

730003 CITY OF TALLAHASSEE ARVAH B.HOPKINS GENERATING STATION xx 0.444

810010 FLORIDA POWER & LIGHT (PMT) MANATEE POWER PLANT xx 0.458

850001 FLORIDA POWER & LIGHT (PMR) FPL / MARTIN POWER PLANT xx 0.449

890003 SMURFIT STONE CORPORATION (US) JSC-FERNANDINA BEACH PAPERBOARD MILL p 0.495

990016 ATLANTIC SUGAR ASSOCIATION ATLANTIC SUGAR MILL x 0.49

990019 OSCEOLA FARMS OSCEOLA FARMS x 0.302

990026 SUGAR CANE GROWERS CO-OP SUGAR CANE GROWERS CO-OP x 0.467

990042 FLORIDA POWER & LIGHT (PRV) RIVIERA POWER PLANT x

990045 LAKE WORTH UTIL. TOM G. SMITH PLANT xxx 0.11

990061 U.S.SUGAR CORP. BRYANT MILL U.S. SUGAR CORP. BRYANT MILL x

1010017 FLORIDA POWER CORP. ANCLOTE POWER PLANT xx 0.488

1030011 FLORIDA POWER CORPORATION FPC-BARTOW PLANT x

1050003 LAKELAND ELECTRIC CHARLES LARSEN MEMORIAL POWER PLANT x

1050004 LAKELAND ELECTRIC C.D. MCINTOSH, JR. POWER PLANT xx 0.215

1050046 MOSAIC - BARTOW BARTOW x 0.144

1050053 MOSAIC - GREEN BAY GREEN BAY PLANT x 0.219

1050055 IMC PHOSPHATES COMPANY SOUTH PIERCE x 0.494

1050059 MOSAIC - NEW WALES NEW WALES p 0.496

1070005 GEORGIA-PACIFIC CORP. PULP/PAPER MILL GEORGIA-PACIFIC CORP. PULP/PAPER MILL p 0.48

1070014 FLORIDA POWER & LIGHT (PPN) PUTNAM POWER PLANT xxx 0.491

1110003 FT PIERCE UTILITIES AUTHORITY FT PIERCE UTIL/H D KING PWR PLNT x

1130003 STERLING FIBERS, INC. STERLING FIBERS, INC. x

1130005 EXXONMOBIL PRODUCTION COMPANY ST REGIS TREATING FAC AND JAY GAS PLANT x 0.39

1230001 BUCKEYE FLORIDA, LIMITED PARTNERSHIP BUCKEYE FLORIDA, LIMITED PARTNERSHIP x 0.284

1290001 TALLAHASSEE CITY PURDOM GENERATING S TALLAHASSEE CITY PURDOM GENERATING STA x

TOTALS 6 31 2 7

c = BART for PM only

p = federally enforceable permit changes made to meet exemption criteria

xx = EGU exemption based on PM only

xxx = EGU exemption based on SO2, NOx, PM; does not rely on CAIR

xxxx = BART satisfied by top-level control for SO2, NOx,PM; does not rely on CAIR106 In summary, of the 4 6 sources that are BART

- eligible, 3 3 were determined to be exempt from
BART, and 7 others have been shut down. The remaining 6 sources were required to complete a BART determination for the applicable units or show that existing controls on the units are the

top

- level control . The following section discusses the results of the BART determinations.

7.8. 4 Determination of BART Requirements for Subject

- to

- BART Sources

Permit applications were received from each of the subject

- to

- BART sources. Below is a summary of the disposition for each of the BART

- eligible sources subject to a BART

determination considering the five factors: (1) the costs of compliance, (2) the energy and non

air quality environmental impacts of compliance, (3) any existing pollution control technology in use at the source, (4) the remaining useful life of the source, and (5) th e degree of improvement in visibility which may reasonably be anticipated to result from the use of such technology

.

The permit s issued for these sources reflect the imposition of BART and are federally

enforceable.

BART

- eligible Emissio ns Units 7.8.4.1 CEMEX

CEMEX, operates an existing portland cement plant with two portland cement lines (Lines 1 and 2). These include: two Polysius GEPOL preheater kilns (Kilns 1 and 2); two clinker coolers and associated raw mills; finish mills; cement and clinker ha ndling equipment; coal handling equipment; silos; and air pollution control devices. The nominal capacity of each kiln is 780,000 tons per year (TPY) of clinker. The facility is located at the existing plant which is located on highway 98, northwest of B rooksville, in Hernando County, Florida. The facility operates the following emissions units subject to the requirements

of Rule 62

- 296.340 (BART) , F.A.C. The BART emissions units are part of Line 1 :

EU No. Emission Unit Description

002 No.1 Kiln Feed System (Baghouse D

- 31)

– Pyroprocessing/Raw Mill System

003 Cement Kiln No. 1 (Baghouse E

- 55)

– Pyroprocessing/Raw Mill System

004 Cement Plant Clinker Cooler No. 1 (Baghouse F

- 18)

– Clinker Handling System

005 Finish Mills No. 1 and No. 2 with two dust collectors (Baghouse G

- 23)

– Finish Mill System

006 Clinker Storage Silo Nos. 1 & 2 (Baghouse F

- 31)

– Clinker Handling System

008 Baghouse No. F

- 17 of Kiln No.1 Blending Silo No. 1

– Cement Products is not

Baghouse No. E

- 36 of Kiln No 1. Blending Silo No. 2

– Cement Products BART

- eligible

is

009 BART

- eligible

Cement Plant STG Silos Dust Unit (Baghouse H

- 3)

– Cement Products

011 Raw Material Storage Silos & Feed System (Baghouses C

- 11, C

- 11A)

Affected Pollutants

T he affected visibility

- impairing pollutants include NO X , PM 10 and SO 2

.107 Project Description and Status

On February 1 , 2007, CEMEX submitted an application to satisfy the BART requirements. With the subsequent additional information received on September 5 , 2007, the Department deemed the application complete. December 3, 2007 , the a pplicant waived the 90 day processing clock until December 17, 2007

. The Department issued a draft air construction Permit No. 0 530010

- 030

- AC on December 17 , 2008 with

BART de terminations.

BART Determinations The BART determination for different units at this facility are basically the top control technology currently being employed

. The kiln was subjected to Prevention of Significant Deterioration (PSD) review and Best Ava ilable Control Technology determination (BACT) review since 1977 one or more times and the permitted values stand up very well compared to recent determinations made throughout the country even for Greenfield sites. We took those

factors into consideratio n and determined it was not necessary to submit a full five factor analysis and determined that the controls in place constituted Best Available Retrofit Technology (BART).

Overall, the controls consist of highly effective sulfur dioxide scrubbing in the calciner, low raw material sulfur, fabric filter baghouses for particulate matter and selective non

- catalytic reduction (SNCR) for nitrogen oxides control.

An identical kiln was built alongside Kiln 1 that underwent BACT in the early 80’s. Both kilns

ha ve since undergone BACT and have been subjected to enforcement actions leading to installation of present controls.

All controls including emission limits are federally enforceable.

BART controls follows:

NO X Kiln Controls

a. To control emissions of nitrogen oxides (NOx), CEMEX shall operate the installed SNCR system as previously authorized by Permits 0530010

- 026

- AC and 0530010

- 34

- AC. : To control emissions of nitrog en oxides (NO X ), CEMEX shall implement one of the following strategies:

b. In lieu of a. above, CEMEX is authorized to install a selective catalytic reduction (SCR) system between the preheater and the raw mill to augment or replace the existing SNCR system. In the event the applicant chooses to install the SCR, installation and compliance

with lim it of 1.2 lb/ton of kiln preheater feed must be completed by October 31, 2013. The SNCR system shall be used in the interim to meet the compliance dates and limits in

Permits 0530010

- 026 and 0530010

- 34

- AC. The injectors may be rearranged as needed

to opt imize the final configuration.

SO 2 Kiln Controls : The present SO 2 control system consisting of dry alkali and lime scrubbing in the kiln system and limestone scrubbing in the raw mill constitutes BART for EU 003. CEMEX shall continue to operate and maint ain Kiln No. 1 as indicated in Permit 0530010

- 003

- AC (PSD

- FL

- 233) and the current Title V permit. No later than January 1, 2014, the permittee108 shall also comply with time

- averaged limits and monitoring requirements discussed further below. [Rule 62

- 296.3 40 (BART), F.A.C.]

PM/PM 10 Kiln Standards
To control emissions of PM from Kiln No. 1 CEMEX shall continue to operate and maintain baghouse E

- 55 as required by Permit 0530010

- 003

- AC (PSD

- FL

- 233) and the current Title V permit. :

To control emissions of PM from Cooler No. 1 CEMEX shall continue to operate and maintain baghouse F

- 18 as required by Permit 0530010

- 003

- AC (PSD

- FL

- 233) and the current Title V permit.

Baghouse Controls

Visibility : The applicant provided a Post

- BART analysis for PM (finish mills) and NO X reductions (kiln) with the addition of Indirect Firing and SNCR, permitted by the Department 12/22/06 (Permit No. 0530010

- 026). These reduced emission rates provided a visibility impact of 0.933dv (8 th highest, 98 th percentile) a nd reduced the number of days above the visibility threshold by approximately 45%. : Each emissions unit identified in the Table above shall control

PM/PM 10 by a baghouse system. Bags/filters in each baghouse control system shall be selected based on a PM design outlet specification of 0.01 grain (gr) per dry standard cubic foot (dscf) and a PM 10 design outlet specification of 0.007 gr/dscf. Compliance shall be

d emonstrated by maintaining the appropriate records and by complying with the mass emission limits, opacity limits and test requirements as indicated in BART Permit 0530010

- 30

- AC. Records may include, but are not limited to, Specifications stated in Purchase Orders, Specifications provided by Suppliers at the time of Delivery, and Records required by the CEMEX O&M Plan.

Pre

- BART and Post

- BART Visibility Impacts

BART

- Eligible

Sources Actual Visibility Impact (dv)* Impact with Reductions (dv)* Change in Visibility Impact (dv)

2001 1.457 0.933 0.524

2002 1.341 0.841 0.500

2003 1.354 0.848 0.506

*98 th percentile109 Review The BART determination summarizes a limit or a technology for different units as follows:

Pollutant NOx Emissions Standard (VE) for Kiln 1:

Emission Limit Averaging

Time Compliance

Method Basis

NO X 2.0 lb/ton clinker 30

- operating day rolling CEMS Applicant Request,

PSD Avoidance and BART

Pollutant SO 2 Emissions Standard (VE) for Kiln 1:

Emission Limit Averaging

Time Compliance

Method Basis

SO 2 0.17 lb/ton clinker 24

- operating hour rolling CEMS BART 15 lb/hr @ 150 TPH of feed

16.5 lb/hr @ 165 TPH of feed

EU No. PM/PM 10 and Visible Emissions Standards (VE) for Kiln 1 and Cooler 1:

Description Emission Limit lb/hr at 150 dry preheater feed lb/hr at 165 TPH dry preheater feed VE

003 Kiln No. 1 0.31 lb/ton clinker 27.0 29.7 10

004 Cooler No. 1 0.15 lb/ton clinker 13.6 14.9 10

PM/PM 10 and Visible Emissions Standards (VE) for the Emissions units listed belo

EU No. w :

Description BART limit lb/hr (3

- hr) BART Opacity

002 No.1 Kiln Feed System (Baghouse D

- 31) Pyroprocessing / Raw Mill System 1.02 5%

005 Finish Mill No. 1 and No. 2 with two bag collectors (Baghouse G

- 23)

– Finish Mill System 9.0 (for each mill) 5%

006 Clinker Storage Silo Nos. 1 & 2 (Baghouse F

- 31) Clinker Handling System 1.45 5%

008 Kiln No 1. Blending Silos [Baghouse No. (E

- 36)(silo 2)] Cement Products 1.02 5%

009 Cement Plant Storage Silos Dust Unit [Baghouse No. (H

- 3)(silos 1

- 5)]

– Cement Products 5.0 5%

011 Raw Material Storage Silos (Baghouse C

- 11)

– Raw Material Handling 1.29 5%

Transfer Belt (Baghouse C

- 11A)

– Raw Material Handling 0.86 5%110 7.8.4.2 FPL Turkey Point

BART

- eligible Emissions Units Florida Power & Light Company (FPL) operates an existing power plant known as the Turkey Point

Power Plant

, which consists of two residual fuel oil and natural gas

- fueled 440 MW fossil fuel steam electrical generators (Units 1 and 2), five fuel oil

- fired black start 2.75 MW diesel peaking generators supporting Units 1 and 2, a natural gas

- fueled 1,150 MW combined cycle unit (Unit 5), and associated equipment. The facility is located in Miami

- Dade County at 9700 SW 344 Street, in Homestead

, Florid a. The facility operates the following emissions units subject to the requirements of Rule 62

- 296.340 (BART), F.A.C.: Boiler 1 (EU

- 001) and Boiler 2 (EU

- 002). Particulate matter emissions are currently controlled from e ach unit by low excess air burners and Universal Oil Products ( UOP ) Air

Correction Division multi

- cyclones with reinjection. The multi

- cyclones consist of two tubular mechanical dust collectors with 695 tubes per collector.

Affected Pollutants

The affected visibilit y

- impairing pollutants include NOX, PM10 and SO 2 . For electric utilities subject to CAIR, only particulate matter is subject to BART review.

Project Description and Status On January 31, 2007, FPL submitted an application to satisfy the BART requirements. With the subsequent additional information received on May 7, 2007, July 6, 2007, September 13, 2007, and January 31, 2008 , the Department deemed the application complete. The Department issued draft air

construction p er mit No. 0250003

- 008

- AC on May 23, 2008. The applicant commented on the draft and entered into negotiations with the Department to make changes. A revised draft permit was issued on October 17, 2008. Additional information was received on November 14, 20 08 and February 13, 2009. Another revised draft permit was issued on March 2, 2009. This version of the draft was published in the Miami Herald on March 13, 2009 and the final permit was issued on April 14, 2009.

BART Determinations Boilers 1 and 2 fire oil as the primary fuel. Bag houses and ESP are generally recognized as the top controls with removal efficiencies greater than 99%. Although feasible, wet scrubbers have not demonstrated equivalent levels of control for PM. Bag hou ses do not appear to be a good choice either, since tests conducted by FPL at the Sanford plant found that particles generated

from the combustion of oil

- based fuels caused considerable plugging of bags in pilot scale tests. The initial visibility modelin g analysis showed that replacing the existing multi

- cyclones with new electrostatic precipitators (ESP) that are designed to meet the current BACT limit of 0.015 lb/MMBtu would only result in a 0.1 deciview (dv) change in visibility at the nearest class 1 area. The applicant also expressed concern that the cost of replacing the existing multi

- cyclones with new control devices would be unreasonably high. The applicant considered the age of the units, the low annual capacity factor (less than 30%), the very high cost of replacing the PM control devices (>$10,000 per ton of PM removed) and the inability to secure a guarantee from

the vendor of a retrofitted control device emission limit of less than 0.03 lb/MMBtu. In order to provide a greater potential impr ovement in visibility at the Class 1 area with a much lower capital investment requirement, the applicant proposed instead to: upgrade the existing multi

cyclones, reduce the allowable PM limit from 0.1 lb/MMBtu to 0.07 lb/MMBtu (along with a111 study of dif ferent fuel additives aimed at possibly further reducing the PM limit to 0.05 lb/MMBtu), reduce the allowable sulfur content of the fuel from 1% to 0.7% (0.77 lb

SO 2 /MMbtu) and to reduce the opacity from 40% to 20% (12

- month rolling average).

Review Step 1

The available retrofit control technologies for these boilers include the following: . Identify all available retrofit control technologies.

**•** Add wet scrubbers following the existing multi

- cyclones.

**•** Add electrostatic precipitators (ESP) following the exist ing multi

- cyclones.

**•** Add bag houses following the existing multi

- cyclones.

**•** Replace the existing multi

- cyclones with new state

- of

- the

- art multi

- cyclones.

Step 2

Bag houses and ESP are generally recognized as the top controls with removal efficiencies greater than 99%. Although feasible, wet scrubbers have not demonstrated equivalent levels of control for PM. Also, bag houses do not appear to be a good choice either, since tests conducted by FPL at the Sanford plant found that particles generated from the combustion of

oil

- based fuels caused considerable plugging of bags in pilot scale tests. . Eliminate technically infeasible options.

All control options identified in Table A are considered technically feasible.

Step 3

. Table A identifies the control efficien cy for each pollutant under each control option

identified.

Step 4

. Table A identifies the rank for total capital costs as well as the rank for overall control effectiveness ($/ton). Energy impacts were considered in the cost analysis. The applicant did not identify any adverse non

- air quality environmental impacts from the control equipment options and did not consider remaining useful life an issue.

Step 5

Based on the BART analysis for the Turkey Point project, meeting an emissions standard of 0.015 lb/MMBtu may technically be achieved by the installation of a new ESP on each of these units; however, the vendor will not provide a guarantee of being able to meet less than 0.3 lb/MMBtu because of issues related to installing a retrofit ESP on these old emissions units. At a $94,000,000 capital investment and greater than $10,000 per ton of PM 10 removed to achieve a 0.1dv reduction in visibility, replacing the existing multi

- clones with a new ESP is not considered to be cost effective. As an alternative, the Department accepts Florida Power Company’s proposal for reducing particulate matter and sulfur di oxide emissions from the current allowable emissions limits of 0.1 lb/MMBtu and 1.0% sulfur fuel, respectively, down to 0.07 lb/MMBtu and 0.7% sulfur fuel. This option results in a modeled improvement in visibility of 0.6 dv at an investment of approximat ely $7,300,000. . Table A identifies the estimated visibility change for each control option based on the

applicant ’s visibility modeling analysis using the CALPUFF model.112 Table A. Control Equipment Analysis

Control Option Control Efficiency Cost Effectiveness 2 Capital Costs 3 Visibility Impacts

Percent 1 Rank $/ton Rank $ Rank dv Change $ /dv Change Rank

New ESP 7 0% PM 10 1 10,623 2 94,000,000 2

- 0.1 0 134,000,000 2

Multi

- cyclone upgrade

and reduced fuel sulfur 30 % PM 10 30% SO 2 2 3,568 1 7,281,340 1

- 0.60 3,200,000 1

Notes:

1. Pe rcent reduction from current permitted limits.

2. Average annual cost per ton removed.

3. Total capital costs for both units.

7.8.4. 3 Progress Energy Crystal River

BART

- eligible Emissions Units Progress Energy Florida operates an existing power plant known as the Crystal River Power Plant

, which

consists of four coal

- fired fossil fuel steam generating (FFSG) units and associated equipment

. The facility is located in Citrus County on Power Line Road, West of U.S. Highway 19

, in Crystal River,

Florid a. The facility operates the following emissions units subject to the requirements of Rule 62

- 296.340 (BART), F.A.C.: Boiler 1 (EU

- 001) and Boiler 2 (EU

- 002). Particulate matter emissions are currently controlled by electrostatic precipitators (ESP).

Affected Po llutants The affected visibility

- impairing pollutants include NO X , PM 10 and SO 2 . For electric utilities subject to CAIR, only particulate matter is subject to BART review.

Project Description and Status On January 31, 2007, Progress Energy Florida submitt ed an application to satisfy the BART requirements. With the subsequent additional information received on August 29, 2007 , the Department deemed the application complete. The Department issued draft air construction p ermit No. 0170004

- 017

- AC on

November 21, 2007. The applicant submitted a new application and a request to withdraw the draft permit on December 24, 2007. Additional information was submitted on October 22, 2008, which made the new application complete. A revised draft permit with BART det erminations was issued on December 18,

2008

. The applicant published the Public Notice of Intent to Issue Permit on January 24, 2009 and submitted comments on January 27, 2009. The final permit with a BART determination was issued on February 26, 2009

.

BART Determinations Boilers 1 and 2 fire coal as the primary fuel. The initial visibility modeling analysis showed that even replacing the existing control devices with new ones that are designed to meet the current

BACT limit of 0.015 lb/MMBtu would not result in more than a 0.15 deciview (dv) change in visibility at the nearest class 1 area. The applicant also expressed concern that the cost of substantially upgrading the existing PM controls or replacing them with new control devices would be extremely costly. Considering the age of the units and the very high cost of replacing113 the PM control devices, the applicant proposed instead to upgrade the existing ESP for Unit 2, reduce their allowable PM limit from 0.1 lb/MMBtu to 0.04 lb/MMBtu (average for bo th units), and to permanently cease operating the units as coal

- fired boilers within seven years of the final compliance date of the BART regulations.

Review

Step 1

. Table A lists the control options that were identified.

Step 2

. All control options identi fied in Table A are considered technically feasible.

Step 3

. Table A identifies the control efficiency for each pollutant under each control option

identified.

Step 4

. Table A identifies the rank for total capital costs as well as the rank for overall cont rol effectiveness ($/ton). Energy impacts were considered in the cost analysis. The applicant did not identify any adverse non

- air quality environmental impacts from the control equipment options but did consider remaining useful life an issue.

Step 5

Based on the BART analysis for the Crystal River project, meeting an emissions standard of

0.015 lb/MMB tu may be achieved by all proposed options; however, it is not cost effective to do so recognizing that these units are nearing the end of their useful life. As an alternative to potentially spending around $150 million to replace the particulate matter c ontrol devices on these old units in order to continue to operate them for many years beyond the BART implementation date, Progress Energy Florida, Inc. has proposed to spend around $50 million to upgrade the existing ESP for Unit 2 in order to meet a prop osed combined emissions limit from Units 1 and 2 of 0.04 lb/MMBtu in order to operate them for only seven years beyond the final BART implementation date. While historical tests on these units at full load and steady

- state operation have shown them capabl e of emitting PM at levels less than 0.04 lb/MMBtu, normal operation tends to vary at levels less than full load, which results in emissions close to the permitted limit of 0.1 lb/MMBtu. If the Units were newer and were going to remain in operation

for at least 20 years, a clear argument could be made that replacing the existing ESPs with new,

state

- of

- the

- art, ESPs would be cost effective. Considering the age of the existing units, it is not reasonable to require the large capital expenditure needed to b ring emissions down to levels achievable by new units and control devices given the very limited remaining useful life. Therefore, the Department accepts Progress Energy Florida, Inc.’s proposal for reducing particulate matter emissions from the current a llowable emissions limit of 0.1 lb/MMBtu down to levels near what has been reported on the last five year’s stack tests (0.04 lb/MMBtu) with a commitment cease operating these old units as coal

- fired boilers by December 31, 2020. The units may be repowere d with a fuel other than coal. Such repowering will require an air construction permit application prior to any changes. . T able A identifies the estimated visibility change for each control option based on the

applicant’s visibility modeling analysis using the CALPUFF model.114 Table A. Control Equipment Analysis

Control Option Control Efficiency Cost Effectiveness 1 Capital Costs 2 Visibility Impacts

Percent Rank $/ton Rank $ Rank dv Change $ /dv Change Rank

Bag House Conversion 94% PM 10 1 30,582 2 71,775,596 2

- 0.150 95,122,400 1

Polishing Bag House (following existing ESP) 88% PM 10 3 40,354 3 73,131,683 3

- 0.110 127,235,127 3

New ESP 90 % PM 10 2 49,274 4 144,223,206 4

- 0.130 127,098,392 2

Rebuilt ESP 85% PM 10 4 12,987 1 28,760,610 1

- 0.100 27,316,290 4

Notes:

1. Average annual cost per ton removed for both units assuming 7 year remaining life.

2. Total capital costs for both units.

7.8.4. 4 PCS White Springs

BART

- eligible Emissions Units The applicant, White Springs Agricultural Chemicals, Inc., d/b/a PCS Phosphate, operates a phosphate fertilizer manufacturing complex in White Springs, Florida. The facility is located at

15843 SE 78 th Street, White Springs, Hamilton County. This site is located about 25 kilometers from the Okefenokee National Wilderness Area, a Class I Area. The facility operates the following listed emissions units that are subject to the requirements of Best Available Retrofit Technology (BART) as listed in Rule 62

- 296.340(BART), F.A.C.:

BART El igible Emissions Units at the PCS Phosphate Facility

EU No. Emissions Unit Description

001 #2 Phoshate Rock Grinder

003 A Defluorinated Phosphate Plant (DFP)

004 X Train (Dical Process)

008 Y Train

- #1 Diammonium Phosphate (DAP) Plant

010 #1 Monoammonium Phosphate (MAP)/DAP

– Storage Building

015 MAP/DAP Shipping & Screening Facility

021 C Sulfuric Acid Plant (SAP)

022 D SAP

032 Z Train

- #2 DAP Plant

038 B DFP

042 DFP Feed Prep

044 A & B DFP Coolers115 054 Molten Sulfur System

062 DFP Silos

064 Swift Creek Mine Rock Dryer

065 Swift Creek Mine Silos

SAP C and D plants use the double absorption process to control sulfur dioxide emissions and

demisters to control sulfuric acid mist emissions.

All of the listed DAP/MAP plants include medium to high efficiency wet scrubbers that use phosphoric acid and then pond water to reduce F and PM from the reactor and granulators. They are also equipped with abatement scrubbers using fresh water for final cleanup. Emissions from the dryers, cool ers, mills and screens are controlled by cyclones, wet scrubbers with phosphoric acid or pond water as the scrubbing medium, and by abatement scrubbers using fresh water.

A and B DFP Coolers (EU 044) and Swift Creek Mine Silos (EU 065) use wet cyclonic

scr ubbers with pond water as the scrubbing medium to control particulate matter emissions.

A and B DFP Plants (EU 003 and 038, respectively) include cross

- flow packed wet scrubber with pond water as the scrubbing medium to control particulate matter emissions

. X Train Dical Process (EU 004) rotary dryer include series of wet venturi and cyclonic scrubbers to control particulate matter emissions.

The #2 Phosphate Rock Grinder (EU 001), X Train (EU 004) limestone handling, the DFP Feed Prep area (EU 042), DFP Product Silos (EU 062) include fabric filter baghouses designed to recover process or product raw materials and to control particulate matter emissions.

Swift Creek Mine Rock Dryer (EU 064) and Swift Creek Mine Silos (EU 065) include wet cyclonic scrubber to control particulate matter emissions. The Rock Dryer is fired primarily with natural gas.

Affected Pollutants The affected visibility

- impairing pollutants include NO X , PM 10 and SO 2

. Project Description and Status

On February 1 , 2007, PCS Phosphate submitted an application to satisfy the BART requirements. With the subsequent additional information received on September 4 , 2007, the Department deemed the application complete. The Department issued a draft air construction Permit No. 0 470002

- 055

- AC on November 13 , 200 7 with BART determinations

. PCS Phosphate filed a petition for formal administrative hearing on January 23, 2008, after their request for extension of time to file an administrative hearing was denied by the Department. PCS Phosphate has been in negotiations with the Department to resolve the outstanding issues related to the proposed BART. PCS Phosphate resolved all the outstanding issues with the Department in letters dated December 16, 2008 and January 20, 2009 to the Department. The Department will issue a revised draft BART construction permit which will incorporate the resolution of the outstanding issues as listed in PCS Phosphate’s December 16, 2008 and January 20, 2009 letters to the Department.

BART Determinations116 SAP Plants : At PCS Phosphate molten elemental sulfur is combusted to produce the source of the gaseous SO 2 used to manufacture sulfuric acid. Thus SO 2 is a valuable important intermediate raw material for the process. The key emissions are SO 2 , sulfur triox ide (SO 3 ) and sulfuric acid mist (SAM or H 2 SO 4 ). All are key intermediate raw materials or products as well as pollutants. NO X and minor amounts of PM from the sulfur source (typically combustion of molten sulfur) will also exit through the SAP stack.

MAP/DAP Plants

The resulting slurry/solids mixture is dried in a fossil fuel fired direct contac t rotary dryer. The dried solids are then screened to remove on size product. The product size material is cooled and then conveyed to storage. The over

- sized and under

- sized materials are crushed and recirculated through the granulator. : At the PCS Phosphate’s MAP/DAP plants; phosphoric acid is reacted with ammonia to produce fertilizer. In the basic ammoniated phosphate process, anhydrous ammonia is reacted with phosphoric acid. The slurry produced by the ammoniation is then sprayed onto a bed of solids in the granulator and additional ammonia (if required) is added to complete the acid neutralization and produced the final product grade.

The key pollut ants from the DAP/MAP plants are fluoride (particulate and gaseous), other PM in the form of DAP and MAP, and also NH 3.

DFP Plants : At PCS Phosphate’s DFP Plants; phosphate rock is reacted with soda ash slurry and limestone. The resulting mixture is intr oduced into a natural gas fired, fluidized bed reactor. As the feed is introduced into the reactor a proportional amount of finished feed is discharged into a fluidized air cooler. In the cooler, material from the reactor and cyclone are cooled and

trans ported to a holding bin. The product is screened and oversize material is sent to grinding roll mills for sizing.

X Train (Dical Process)

The initial modeling analysis indicated maximum visibility impacts of 3.07 dv during the period

2001

- 2003. The 8 th highest visibility impairment value for ea ch of the three years is over the comparison threshold of 0.5 dv (range 1.63

- 1.77). The 22 nd highest impairment is 1.72 dv. In addition, there are 87, 93 and 91 days (2001

- 2003) predicted to have visibility impairment of over 0.5 dv. The primary focus o f the determination was on the effects of lower SO 2 , NO X and

PM 10 emissions. : In this process, limestone reacts with phosphoric acid in a pug mill. The resulting slurry is then dried in a dir ect contact rotary dryer fired with either natural gas or fuel oil. The rotary dryer is controlled by a system of wet venturi and cyclonic scrubbers. The

dried solids are then screened to remove on

- size product. The product size material is conveyed

to storage. The over

- sized and under

- sized materials are crushed and recirculated to the pug

mill.

BART determinations are conducted on certain emission units constructed between 1962 and 1977, during which time top/down case

- by

- case determinations for best available control technology (BACT) were not conducted. BACT is a requirement for projects subject to the rules for the prevention of significant deterioration (PSD) promulgated by EPA in January 1975. However since 1977 and most recently since the reference perio d 2001

- 2003 Line 1 has been subjected to a number of reviews that resulted in the installation of control equipment or inclusion of federally enforceable emission limits characteristic of BACT.117 The BART process is described in 40 Code of Federal Regulation (CFR) Part 51, Appendix Y as

follows:

Step 1

- Identify all available retrofit control technologies;

Step 2

- Eliminate technically infeasible options;

Step 3

- Evaluate control effectiveness of remaining control technologies;

Step 4

- Evaluate impacts a nd document the results; and

Step 5

- Evaluate visibility impacts.

According to 40 CFR 51, Appendix Y, paragraph IV.D.9, there are circumstances under which it is not necessary to comprehensively complete each step of the BART analysis. Under such

circums tances, it is allowable to skip the remaining analyses in this section, including the visibility analysis in Step 5. Furthermore, according to 40 CFR 51, Appendix Y, paragraph II.A.2, for an emissions unit which began operation within the 1962

– 1977 time w indow, but was modified (major) after August 7, 1977 such that controls were installed, the State will take this into account during the review process and may find that the level of controls already in place

are consistent with BART.

The applicant submitt ed the application on the basis of the original design, and the subsequent

PSD/BACT review of similar emissions units at other facilities, like C.F. Industries SAPs B, C (2004

- 2007). The details of the previous reviews and specific additional enforceable conditions are given in the technical document accompanying the BART permit. The Department finds that the levels of controls already in place are consistent with BART. The

SAP emissions limits for SO 2 can be further reduced by slight modifications to the converter as explained in the technical document accompanying the BART permit. The following emission limits comprise BART and were incorporated into the final BART permit:

SAP Plants

SO 2 Emission Standards:

Source Sulfur Dioxide (SO 2 ) Emission Limits

SAP C 1 3.5 lb SO 2 /ton of 100% H 2 SO 4 and 335.4 lb/hr, 24

- hour rolling average by

CEMS 2

SAP D 3.5 lb SO 2 /ton of 100% H 2 SO 4 and 335.4 lb/hr, 24

- hour rolling average by

CEMS 2

1 The applicant is authorized to modify the converter tower to comply with the new BART

emission limit for SO 2

.

2 This BART determination requires an SO 2 CEMS to be used to demonstrate continuous compliance with the SO 2 emission standards and limitations specified above.

NO X Emission Standard

Source s :

Nitrogen Oxides (NO X as NO 2 ) Emiss ion Limits

SAP C (EU 0.14 lb NO X /ton of 100% H 2 SO 4 . Testing using EPA Method 7E shall118 021) be done upon request by the Department.

SAP D (EU 022) 0.14 lb NO X /ton of 100% H 2 SO 4 . Testing using EPA Method 7E shall be done upon request by the Department.

As determined by EPA Method 9, visible emissions from the two SAP shall not exceed the current 10% opacity limits based on a 6

- minute average. PM/PM 10 - Opacity (VE) Standard

DAP/MAP Plants

PM/PM 10 Emissions Standard

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits based on the average of three test runs as determined by EPA Me thods 5. :

Source Particulate (PM/PM 10 ) Emission Limits

DAP/MAP Y (EU 008) 9.2 lb/hr by annual 3

- hr stack test

DAP/MAP Z (EU 032) 9.2 lb/hr by annual 3

- hr stack test

DAP/MAP Y

– Storage Building (EU 010) 4.7 lb/hr by annual 3

- hr stack test

DAP/MAP

– Shipping & Screening (EU 015) 1.46 lb/hr by annual 3

- hr stack test

To control emissions of particulate matter (PM/PM 10 ), the permittee shall operate and maintain the existing process and pollution control technology for each of the DAP/MAP plants. The

ap plicant is authorized to make improvements to the process and pollution control equipment for the purposes of reliably meeting the new BART PM/PM 10 emission limits given above.

SO 2 and NO X Controls and Emission Standards

To control SO 2 and NO X emissions from DAP/MAP Plants Y and Z, the permittee shall continue to use natural gas in the dryers with 1.5 weight percent sulfur fuel oil as backup. No numerical

emission limit is established. :

DFP Plants

PM/PM 10 Emissions Standard

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits based on the average of three test runs as determined by EPA Methods 5. :

Source Particulate (PM/PM 10 ) Emission Limits

A DFP (EU 003) 14.1 lb/hr by annual 3

- hr stack test

B DFP (EU 038) 14.1 lb/hr by annual 3

- hr stack test

DFP Feed Prep (EU 042) 4.5 lb/hr by annual 3

- hr stack test119 Source Particulate (PM/PM 10 ) Emission Limits

A and B DFP Coolers (EU 044) 17.5 lb/hr by annual 3

- hr stack test

To control emissions of particulate matter (PM/PM 10 ), the permittee shall operate and maintain the existing process and pollution control technology for each of the DAP/MAP plants. The applicant is authorized to make improvements to the process and pollution control equipment fo r the purposes of reliably meeting the new BART PM/PM 10 emission limits given above.

SO 2 and NO X Controls and Emission Standards

To control SO 2 and NO X emissions from A and B DFP Plants, the permittee shall continue to use natural gas in the dryers with 1.5 weight percent sulfur fuel oil as backup. No numerical

emission limit is established. :

X Train (Dical Process):

PM/PM 10 Emissions Standard

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits based on the average of three test runs as determined by EPA Methods 5. :

Source Particulate (PM/PM 10 ) Emission Limits

X Train (EU 004) 18 lb/hr by annual 3

- hr stack test

SO 2 and NO X Controls and Emission Standards

To control SO 2 and NO X emissions from X Train, the permittee shall continue to use natural gas in the dryers with 1.5 weight percent sulfur fuel oil as backup. No numerical emission limit is

established. :

Swift Creek Mine:

PM/PM 10 Emissions Standard

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits based on the average of three test runs as determined by EPA Methods 5. :

Source Particulate (PM/PM 10 ) Emission Limits

Swift Creek Mine Rock Dryer (EU 064) 17.3 lb/hr by annual 3

- hr stack test

Swift Creek Mine Silos (EU 065) 6.27 lb/hr by annual 3

- hr stack test

SO 2 and NO X Controls and Emission Standards

To control SO 2 and NO X emissions from the Swift Creek Mine Rock Dryer, the permittee shall continue to use natural gas in the dryers with 1.5 weight percent sulfur fuel oil as backup. No

numerical emission limit is established. :

Shipping Units120 Particulate Matter Emissions Limit : A Visible Emissions Standard of 5% Opacity for each baghouse.

7.8.4. 5 City of Tallahassee Purdom

BART

- eligible Emissions Units The City of Tallahassee operates the existing Sam O. Purdom Generating Station, which is an existing power plant located in Wakulla County at 667 Port Leon Drive in St. Marks, Florida. Unit 7 at this facility is a BART

- eligible unit that is fired primarily with fuel oil and natural gas. The unit began operation in 1966 and is a 621 MMBtu per hour steam generator paired with a nominal 44 MW steam

electrical generator.

Affected Pollutants The affected visibility

- impairing pollutants include NO X , PM 10 and SO 2 . EPA determined that CAIR satisfied the BART requirements for NO X and SO 2.
Project Description and Status On January 31 , 2007, the City of Tallahassee submitted an application to satisfy the BART requirements.

T he application was made complete with additional information provided on March 4, 2007 . The Department issued a draft air construction Permit No. 120001

- 008

- AC on May 24, 2007 requiring that Unit 7 permanently cease operation no later than December 31, 2013 to satisfy BART

. No comments were received on the draft permit and a final permit was issued on September 11, 2007 requiring permanent shutdown of Unit 7.

BAR T Determination The following permit condition represents the BART determination for Unit 7 at the Purdom Generating Station:

Review Because the applicant requested a complete and permanent shut

- down of the unit thus eliminating all emissions from the un it, no further BART analysis was required.

BART

- eligible Emissions Units 7.8.4. 6 Tampa Electric Big Bend

Tampa Electric Company (TEC) operates four solid fuel

- fired electrical generating units (EGUs) at its Big Bend Station locoated in North Ruskin, Hillsborough County, Florida. The Big Bend Station is a nominal121 2,028 MW electric generation facility, with units 1, 2, 3, and 4 having maximum heat input rates of 4,037, 3,996, 4,115, and 4,330 million British thermal units per hour (MMBtu/hr), respectiv ely. The primary fuel is coal. Units 1, 2, and 3, meet the requirements of a BART

- eligible unit.

Affected Pollutants The affected visibility

- impairing pollutants include NO x , PM10 and SO 2

.
BART Determination Appendix Y to Part 51 provides guidelines for BART determinations under the regional haze

rule. It states that if a BART

- eligible source has controls already in place which are the most stringent controls available, then it is not necessary to comprehensively complete the BART analysis. Further, the preamble to the rule states if a BART

- eligible source was modified after August 7, 1977, and if such a modification was a major modification that resulted in the installation of controls, “ the State will take that into account during the review proces s and may find that the level of controls already in place are consistent with BART.” Based on the review below, the DEP conclude s that the TEC BART

- eligible emission units 1, 2, and 3 all currently have the most stringent controls for SO 2 , NOx, and PM10. As such, the existing controls represent the BART determination. Exhibit 2 contains the source’s request for this determination, along with its analysis of existing controls.

Review Big Bend Station Units 1, 2, and 3 are presently equipped with a comprehensive suite of

emission control systems to reduce emissions of PM 10 , S O 2 , and NOx. TEC entered into agreements with the DEP and the U.S. EPA to reduce emissions at the Big Bend Stations. These judicially enforceable agreements (DEP’s Consent Fina l Judgment and EPA’s Consent Decree) required minimization and monitoring of PM emissions, upgrades to the FGD scrubbers, and reductions in NOx emissions through the installation of SCR systems.

Electrostatic Precipitators

– PM10 Controls Units 1, 2, and 3 are each equipped with electrostatic precipitators (ESPs). Through the Consent

Decree and Consent Final Judgment , TEC was required to complete a Best Available Control Technology (BACT) analysis for upgrading each ESP. This analysis resulted in physica l upgrades to the ESPs and development of Best Operational Practices (BOPs), resulting in a high

level of PM removal and low emission rates. The PM emission limit is 0.03 lb/MMBtu.

Wet Flue Gas Desulfurization

– SO 2 Controls Units 1, 2, and 3 are each eq uipped with wet flue gas desulfurization (FGD) scrubbers to control

SO 2 emissions. The Consent Decree and Consent Final Judgment required upgrades to the scrubbers resulting in high SO 2 removal rates. Each unit is required to remove at least 95 percent

o f the SO 2 entering the FGD scrubbers.

Selective Catalytic Reduction

– NOx Controls Units 1, 2, and 3 are each equipped with selective catalytic reduction (SCR) technology to

control NOx emissions. The Consent Decree and Consent Final Judgment required re ductions in NOx emission rates. TEC installed SCR control systems on each unit, resulting in high NOx

removal rates. The NOx emissions limit is 0.12 lb/MMBtu.122 Conclusion

The DEP concludes that the existing controls and emissions limits imposed on the T EC Big Bend units 1, 2, and 3, as required by the Consent Decree and Consent Final Judgment, are the top level of control for this source. Further, these controls are BACT

- level and meet the presumptive BART levels contained in the regional haze rule.123 

### 8.0 REASONABLE PROGRESS GOALS

The Regional Haze Rule at 40 CFR section 51.308(d)(1) requires States to establish reasonable progress goals for each Class I area within the state (expressed in deciviews) that provide for reasonable progress towards achievi ng natural visibility. In addition, USEPA released guidance on June 7, 2007 to use in setting reasonable progress goals. The goals must provide improvement in visibility for the most impaired days, and ensure no degradation in visibility for

the least im paired days over the State Implementation Plan (SIP) period. The state must also provide an assessment of the number of years it would take to attain natural visibility conditions if improvement continues at the rate represented by the reasonable progress goal.

In accordance with the requirements of 40 CFR §51.308(d)(1), this Regional Haze Implementation Plan establishes reasonable progress goals for each Class I area in Florida . To calculate the rate of progress represented by each reasonable progress g oal, DEP compared baseline visibility conditions to natural visibility conditions in each class I area and determined the uniform rate of visibility improvement (in deciviews) that would need to be maintained during each implementation period in order to a ttain natural visibility conditions by 2064. Through VISTAS, we modeled the expected visibility improvements under existing federal and state regulations, BART determinations in Florida and neighboring states, and any additional

control measures found to be reasonable to implement in this review period. All of these

elements are part of the long

- term strategy. The se modeling results were used to set the reasonable progress goal s

. Since not all expected reductions were included in the final modeling

runs (due to timing of the runs to be complete in time for SIP submittals)

, reductions will likely

be greater when all BART reductions and reasonable measures are taken into account.

The reasonable progress goals shown in Table 8.0

- 1 reflect the results of the final VISTAS modeling analysis for each of the Florida Class I areas. Visibility improvement is expected to follow the nominal uniform rate of progress for achieving the long

- te rm goal by 2064.124 Table 8.0

- 1. Florida Reasonable Progress Goals

Visibility (dv) Baseline (00

- 04) Natural Conditions 2018 Reasonable Progress

Goals
Class I Area Best

20% Worst

20% Best

20% Worst

20% Best 20% Worst 20%

Chassahowitzka 15.51 25.75 5.91 11.03 13.91 22.27 Everglades 11.69 22.30 5.21 12.09 11.46 19.90

Saint Marks 14.37 26.31 5.39 11.67 12.80 23.01

Worst 20 Percent Days

Class I Area Uniform Rate of Progress (dv/yr) 2018 Uniform

Rate of Progress (dv) 2018 Predicted

Rate of Progress (dv/yr) 2018 Predicted Visibility (dv)

Chassahowitzka 0.25 22.31 0.25 22.27 Everglades 0.17 19.92 0.17 19.90

Saint Marks 0.24 22.89 0.24 23.01

Best 20 Percent Days

Class I Area Uniform Rate of Progress (dv/yr) 2018 Uniform

Rate of Progress (dv) 2018 Predicted

Rate of Progress (dv/yr) 2018 Predicted Visibility (dv)

Chassahowitzka 0.00 15.51 0.11 13.91 Everglades 0.00 11.69 0.02 11.46

Saint Marks 0.00 14.37 0.11 12.80



### 9.0 MONITORING STRATEGY

The State Implementation Plan is to be accompanied by a strategy for monitoring regional haze visibility impairment. Specifically, the Regional Haze Rule states at 40 CFR 51.308(d)(4):

“(4) Monitoring strategy and other implementation plan requirements. The State must submit with the implement ation plan a monitoring strategy for measuring, characterizing, and reporting of regional haze visibility impairment that is representative of all mandatory Class I Federal areas within the State. This monitoring strategy must be

coordinated with the monit oring strategy required in §51.305 for reasonably attributable visibility impairment. Compliance with this requirement may be met through participation in the IMPROVE network. The implementation plan must also provide for the following:125 (i) The establishme nt of any additional monitoring sites or equipment needed to assess whether reasonable progress goals to address regional haze for all mandatory Class I Federal areas within the State are being achieved. (ii)

- (vi) [Other implementation plan requirements th at pertain to reporting and use of monitoring data and an emission inventory.]”

Such monitoring is intended to provide the data needed to satisfy four objectives:

1. Track the expected visibility improvements resulting from emissions reductions

identified in this SIP.

2. Better understand the atmospheric processes of importance to haze

3. Identify chemical species in the ambient particulate matter and relate them to

emissio ns from sources

4. Evaluate regional air quality models for haze and construct relative response factors (RRFs) for using those models

The primary monitoring network for regional haze, both nationwide and in Florida , is the

IMPROVE network. Given that IMPROV E monitoring data from 2000

- 2004 serve as the baseline for the regional haze program, the future regional haze monitoring strategy must necessarily be based on, or directly comparable to, IMPROVE. The IMPROVE measurements provide the only long

- term record available for tracking visibility improvement or degradation

and therefore Florida intends to rely on the IMPROVE network for complying with the regional haze monitoring requirement in the Regional Haze Rule.

There are currently 3 IMPROVE sites in the St ate. In addition, as Table 1 shows, an IMPROVE site just across the border in Georgia serves as the monitoring site for both the Okefenokee and

Wolf Island Class 1 areas , both of which lie in Georgia

.

Class I Area IMPROVE Site Designation

Everglades National Park (FL) EVER

Chassahowitzka Wilderness Area (FL) CHAS

St. Marks Wilderness Area (FL) SAMA

Okefenokee Wilderness Area (GA) OKEF

Wolf Island Wildlife Refuge (GA) OKEF

Table 1. Class I areas in Florida and IMPROVE monitors that represent conditions at

each of them.

In addition, DEP and the local air agencies in the state operate a fairly comprehensive PM2.5 network of the filter based Federal reference method monitors, Federal equivalent method

continu ous monitors (BAMs) and continuous mass monitors (TEOMs), filter based speciated

monitors. A map of the various locations around the State is included in Figure 9.1. These126 PM2.5 measurements help the DEP characterize air pollution levels in areas across t he state, and therefore aid in the analysis of visibility improvement in and near the Class I areas.

Figure 9. 1 PM2.5 Monitoring Network in Florida

The IMPROVE measurements are central to Florida ’s regional haze monitoring strategy, and it is difficult to visualize how the objectives listed above could be met without the monitoring provided by IMPROVE. Any reduction in the scope of the IMPROVE network in Florida would jeopardize the State’s abilit y to demonstrate reasonable progress toward visibility improvement

in some of its Class I areas.

Data produced by the IMPROVE monitoring network will be used nearly continuously for preparing the 5

- year progress reports and the 10

- year SIP revisions, eac h of which relies on analysis of the preceding five years of data. Consequently, the monitoring data from the IMPROVE sites needs to be readily accessible and to be kept up to date. Presumably, IMPROVE will continue to process information from its own meas urements at about the same pace and with the same attention to quality as it has shown in the recent past.127 

### 10.0 INTERSTATE CONSULTATION

The VISTAS states have jointly developed the technical analyses to define the visibility improvement by 2018 under exi sting federal and state regulations compared to the uniform rate of progress, SO 2 Areas of Influence for each Class I area, and methods to prioritize contributions from individual sources within the Areas of Influence. The states collectively accept the

c onclusions of these analyses.

In December 2006 the VISTAS State Air Directors held their first formal consultation meeting to review the Base G modeling results and the SO 2 Areas of Influence analyses. The Air Directors agreed to look at reasonable control measures for sources on the lists for the SO 2 Areas of Influence. Each state would consider sources within their state and would identify sources in neighboring states t hat they would like to have that neighboring state consider. States acknowledged that the review process would differ among states since some Class I areas are projected to see visibility improvements near the uniform rate of progress while most Class I

a reas are projected to have greater improvements than uniform rate of progress.

In April 2007, representatives of all the Regional Planning Organizations (RPO), including VISTAS, met with the federal land managers (FLM) in Lakewood, Colorado to brief them on the technical work completed for the regional haze SIPs. The FLM’s and the RPO’s consulted on what needed to be in the regional haze SIPs. A summary of the meeting is located in Exhibit 3.

In May 2007

, the VISTAS State Air Directors met for their s econd formal interstate consultation. States shared their lists of sources in their state and neighboring states for each Class I area. They also shared their criteria for listing sources and their plans for further

interstate consultation.

In June 200 7, the VISTAS states met with the Federal Land Managers specifically for the purpose of consulting on the state’s visibility plans. A summary of this meeting is included in

Exhibit 3

.

The DEP has evaluated the impact of Florida sources on Class I ar eas in neighboring states

. Both Georgia and Louisiana have class 1 areas within 300 kilometers of Florida sources. Considerable progress in improving visibility is expected in each of these Class I areas from existing regulatory programs (e.g. CAIR, etc. ). The DEP has consulted with the responsible states regarding its impacts on these Class I areas and the strategy we have for further reducing Florida source’s impact on visibility in these areas. Consultation reports and letters may be found

in Exhibit 3

.



### 11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN

REVISIONS

40 CFR section 51.308(f) requires the DEP to revise its regional haze implementation plan and submit a plan revision to USEPA by July 31, 2018

, and every ten years thereafter. In accordance128 wit h the requirements listed in Section 51.308(f) of the federal rule for regional haze, Florida commits to revising and submitting this regional haze implementation plan by July 31, 2018 and every ten years thereafter.

In addition, Section 51.308(g) require s periodic reports evaluating progress towards the reasonable progress goals established for each mandatory Class I area. In accordance with the requirements listed in Section 51.308(g) of the federal rule for regional haze, the DEP commits to submitting a report on reasonable progress to USEPA every five years following the initial submittal of the SIP. The report will be in the form of a SIP revision. The reasonable progress report will evaluate the progress made towards the reasonable progress goal f or each mandatory

Class I area located within Florida and in each mandatory Class I area located outside Florida which may be affected by emissions from within Florida

.

The requirements listed in 51.308(g) include the following:

1. Description of the status of implementation;

2. Summary of emission reductions achieved thus far, including especially the status of implementation of the CAIR compliance plans for EGUs compared to the control assumed in the modeling. [The DEP recognizes that the 2018 projecti ons of EGU controls from the IPM runs represent one solution to how the CAIR requirements will be met. By the time of the first periodic report, the DEP anticipates that the actual compliance strategy for the various utility companies will be much more de fined. An assessment of those actual compliance plans will be done for the first periodic report.]

3. Assessment of changes in visibility conditions at each class I area (current vs. baseline), expressed as 5

- year averages of annual values for 20 percent bes t and worst days;

4. Analysis of emission changes over the 5

- year period, identified by source or activity;

5. Analysis of any siginificant changes in or out of the State which have impeded progress;

6. Assessment of the sufficiency of the implementation plan to me et RPGs;

7. Review and any modifications to our visibility monitoring plan. All requirements listed in 51.308(g) shall be addressed in the SIP revision for reasonable

progress.

DEP also commits to ongoing consultation with the FLMs throughout the implementat ion process, including annual discussion of the implementation process and the most recent IMPROVE monitoring data and VIEWS data.

There are several technical improvements that are recommended in the emissions inventory and air quality models that are use d to support regulatory decisions for regional haze. These improvements recommended, as funding is available, to support the next long term strategy. The following is an overall summary; Appendix K contains a fuller discussion of possible technical improv ements.

First and foremost, continued improvements are needed in the integrated one

- atmosphere air quality models that are used to project air quality responses to emissions reductions. As our understanding of partitioning between gaseous and aerosol pha ses improves, this understanding needs to be reflected in the models. Improvements can also be made in how the models handle individual pollutants. Sulfate performance for the CMAQ regional air quality model is good129 overall. However sulfate deposition i s frequently overestimated in the models, particularly in the summer months. At the coastal sites, when winds are blowing from the Gulf of Mexico or Atlantic Ocean, CMAQ underestimates measured sulfate at the monitors. CMAQ’s processes

also should be rev iewed for sulfate formation over water. Nitrate is overestimated by the model in the winter and underestimated in the summer, although summer monitored values of nitrate are very low. Additional improvements in seasonal allocation of ammonia emissions wo uld improve model estimates of ammonium nitrate formation. Organic carbon is generally underestimated in the summer months. Improvements are needed in the characterization of both primary carbon emissions and formation of secondary organic carbon.

Oth er improvements needed include better tools for organic carbon source apportionment, and more consistent measurement techniques between rural and urban monitoring networks. To improve our understanding of the contribution of fire from natural forest fires , prescribed burning, land clearing, and agricultural burning, states need improved record keeping. Additional improvements to international emissions inventory are also needed, to improve our understanding of boundary conditions for our modeling domain a nd of the contributions from international emissions to pollutant concentrations at the VISTAS Class I areas.



### 12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN

Depending on the findings of the five

- year progress report, Florida commits to taking one o f the actions listed in 40 CFR section 51.308(h). The findings of the five

- year progress report will determine which action is appropriate and necessary.

List of Possible Actions

– 40 CFR section 51.308(h)

1) DEP determines that the existing SIP requires n o further substantive revision in order to achieve established goals. DEP provides to the Administrator a negative declaration that

further revision of the SIP is not needed at this time. 2) DEP determines that the existing SIP may be inadequate to ensure rea sonable progress due to emissions from other states which participated in the regional planning process.

DEP provides notification to the Administrator and the states that participated in regional planning. DEP collaborates with states through the regional planning process to address

the SIP’s deficiencies. 3) DEP determines that the current SIP may be inadequate to ensure reasonable progress due to emissions from another country. DEP provides notification, along with available information, to the Administrat or. 4) DEP determines that the existing SIP is inadequate to ensure reasonable progress due to

emissions within the state. DEP will revise its SIP to address the plan’s deficiencies within one year.